dihedral_xyz_gan_mk1.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "import matplotlib\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from rdkit import Chem\n",
    "from rdkit.Chem import AllChem, rdMolTransforms, rdmolops\n",
    "import os\n",
    "import fnmatch\n",
    "import numpy as np\n",
    "import deepchem as dc\n",
    "from scipy.sparse import csr_matrix\n",
    "from sklearn.preprocessing import OneHotEncoder\n",
    "from sklearn.mixture import GMM\n",
    "import random\n",
    "from tflearn.activations import leaky_relu\n",
    "from deepchem.utils.rdkit_util import get_xyz_from_mol\n",
    "from deepchem.feat.rdkit_grid_featurizer import convert_atom_to_voxel, compute_centroid, rotate_molecules\n",
    "from tensorflow.contrib.distributions import Normal\n",
    "np.set_printoptions(precision=4)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from deepchem.feat.graph_features import ConvMolFeaturizer\n",
    "from deepchem.feat.adjacency_fingerprints import AdjacencyFingerprint"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def featurize_mols(mol_files, max_n_atoms, n_bonds=5, get_angle=True):\n",
    "    featurizer = AdjacencyFingerprint(max_n_atoms=max_n_atoms)\n",
    "    features = []\n",
    "    for idx, mol_file in enumerate(mol_files):\n",
    "        if idx % 32 == 0:\n",
    "            print(idx)\n",
    "        try:\n",
    "            if \".pdb\" in mol_file:\n",
    "                mol = Chem.MolFromPDBFile(mol_file)\n",
    "            elif \"mol\" in mol_file:\n",
    "                mol = Chem.MolFromMol2File(mol_file)\n",
    "            else:\n",
    "                get_angle = False\n",
    "                mol = Chem.MolFromSmiles(mol_file)\n",
    "            if mol is None:\n",
    "                features.append(None)\n",
    "                continue\n",
    "            \n",
    "            if get_angle:\n",
    "                mol_xyz = get_xyz_from_mol(mol)\n",
    "                centroid = compute_centroid(mol_xyz)\n",
    "                mol_xyz -= centroid\n",
    "                temp = np.zeros((max_n_atoms, 3))\n",
    "                try:\n",
    "                    temp[:mol_xyz.shape[0]] = mol_xyz\n",
    "                except:\n",
    "                    features.append(None)\n",
    "                    continue\n",
    "                mol_xyz = temp\n",
    "                c = mol.GetConformer(0)\n",
    "\n",
    "            else:\n",
    "                mol_xyz = np.zeros((max_n_atoms,3))\n",
    "                AllChem.EmbedMultipleConfs(mol, 1)\n",
    "                c = mol.GetConformer(0)\n",
    "                for aid in range(mol.GetNumAtoms()):\n",
    "                    pos = c.GetAtomPosition(aid)\n",
    "                    mol_xyz[aid] = [pos.x, pos.y, pos.z]\n",
    "\n",
    "            torsions = []\n",
    "            torsion_tuples = []\n",
    "\n",
    "            torsion_matrix = np.zeros((n_bonds,1))\n",
    "            torsion_indices = np.zeros((n_bonds, max_n_atoms, 4)).astype(np.uint8)\n",
    "            subgraphs = np.zeros((n_bonds, max_n_atoms, max_n_atoms)).astype(np.uint8)\n",
    "            atom_index_of_bond = np.zeros((n_bonds, max_n_atoms)).astype(np.uint8)\n",
    "            rot_vectors = np.zeros((n_bonds, 3))\n",
    "            \n",
    "            #mol_xyz = rotate_molecules([mol_xyz])[0]\n",
    "\n",
    "            idx = 0\n",
    "            for bond in mol.GetBonds():\n",
    "                if bond.IsInRing(): \n",
    "                    continue\n",
    "                def calc_torsions(atom_i, atom_j, bond_idx):        \n",
    "                    exist_dihed = False\n",
    "                    for neighbor_j in atom_j.GetNeighbors():\n",
    "                        if neighbor_j.GetIdx() == atom_i.GetIdx():\n",
    "                            continue\n",
    "\n",
    "                        dihed_idx = 0\n",
    "                        for neighbor_i in atom_i.GetNeighbors():\n",
    "                            if neighbor_i.GetIdx() == atom_j.GetIdx():\n",
    "                                continue\n",
    "\n",
    "                            exist_dihed=True\n",
    "                            torsion_tuple = (neighbor_i.GetIdx(), atom_i.GetIdx(), atom_j.GetIdx(), neighbor_j.GetIdx())\n",
    "\n",
    "                            if get_angle:\n",
    "                                torsion_matrix[bond_idx][dihed_idx] = rdMolTransforms.GetDihedralRad(c, *torsion_tuple)\n",
    "                            torsion_indices[bond_idx][torsion_tuple[0]][dihed_idx*4] = 1\n",
    "                            torsion_indices[bond_idx][torsion_tuple[1]][dihed_idx*4+1] = 1\n",
    "                            torsion_indices[bond_idx][torsion_tuple[2]][dihed_idx*4+2] = 1\n",
    "                            torsion_indices[bond_idx][torsion_tuple[3]][dihed_idx*4+3] = 1\n",
    "                            \n",
    "                            broken_mol = rdmolops.FragmentOnBonds(mol, [bond.GetIdx()], addDummies=False)\n",
    "                            molfrags = rdmolops.GetMolFrags(broken_mol)\n",
    "                            if bond.GetEndAtom().GetIdx() in molfrags[0]:\n",
    "                                atoms_to_rotate = molfrags[0]\n",
    "                            else:\n",
    "                                atoms_to_rotate = molfrags[1]\n",
    "\n",
    "\n",
    "                            subgraph = np.zeros((max_n_atoms, max_n_atoms)).astype(np.uint8)\n",
    "                            for atom_idx in atoms_to_rotate:\n",
    "                                subgraph[atom_idx,atom_idx] = 1\n",
    "                            subgraphs[bond_idx, :, :] = subgraph\n",
    "                            atom_index_of_bond[bond_idx, atom_j.GetIdx()] = 1\n",
    "                            \n",
    "                            pos_i = c.GetAtomPosition(atom_i.GetIdx())\n",
    "                            pos_j = c.GetAtomPosition(atom_j.GetIdx())\n",
    "                            pt_i = np.array([pos_i.x, pos_i.y, pos_i.z])\n",
    "                            pt_j = np.array([pos_j.x, pos_j.y, pos_j.z])\n",
    "                            vec = pt_j - pt_i\n",
    "                            vec = vec / np.linalg.norm(vec)\n",
    "                            rot_vectors[bond_idx] = vec\n",
    "                \n",
    "                            bond_idx += 1\n",
    "                    return(bond_idx)\n",
    "                \n",
    "                if idx >= n_bonds: \n",
    "                    break\n",
    "                idx = calc_torsions(bond.GetBeginAtom(), bond.GetEndAtom(), idx)\n",
    "            \n",
    "            if idx == 0:\n",
    "                features.append(None)\n",
    "                continue \n",
    "                \n",
    "            graph_feat = featurizer.featurize([mol])[0]           \n",
    "            features.append((mol_file, mol_xyz, torsion_indices, torsion_matrix, subgraphs, atom_index_of_bond, rot_vectors, graph_feat))\n",
    "        except:\n",
    "            features.append(None)\n",
    "    return(features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1041,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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    }
   ],
   "source": [
    "import pickle\n",
    "max_n_atoms = 48\n",
    "D=16\n",
    "S=32\n",
    "feature_file = \"./dihed_xyz_features_pdbbind.pkl\"\n",
    "#if not os.path.exists(feature_file):\n",
    "if 1== 1:\n",
    "    pdbbind_dir = \"/home/evan/Documents/deep_docking/datasets/v2015/\"\n",
    "    def find_files(directory, pattern):\n",
    "        for root, dirs, files in os.walk(directory):\n",
    "            for basename in files:\n",
    "                if fnmatch.fnmatch(basename, pattern):\n",
    "                    filename = os.path.join(root, basename)\n",
    "                    yield filename\n",
    "    ligand_files = []\n",
    "    for f in find_files(pdbbind_dir, \"*ligand.mol2\"):\n",
    "        ligand_files += [f]\n",
    "    ligand_files = ligand_files[:] + [\"CCCC\"]*32 #[\"/home/evan/Documents/deep_docking/alanine_dipeptide.pdb\"]*S  \n",
    "    features = featurize_mols(ligand_files, max_n_atoms, n_bonds=D)\n",
    "    with open(feature_file, \"wb\") as f:\n",
    "        pickle.dump(features, f, protocol=2)\n",
    "else:\n",
    "    with open(feature_file, \"rb\") as f:\n",
    "        features = pickle.load(f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1042,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6888\n"
     ]
    }
   ],
   "source": [
    "features = [f for f in features if f is not None]\n",
    "print(len(features))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1043,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'/home/evan/Documents/deep_docking/datasets/v2015/4wks/4wks_ligand.mol2'"
      ]
     },
     "execution_count": 1043,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features[4][0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2104,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "tf.reset_default_graph()\n",
    "\n",
    "alpha = 0.01\n",
    "n_layers = 0\n",
    "\n",
    "S = 32\n",
    "\n",
    "B = max_n_atoms\n",
    "D = 16\n",
    "p = 75\n",
    "\n",
    "z_dim = 32\n",
    "\n",
    "L_list = [p, 16, 64, 128, 256]\n",
    "\n",
    "dihed_per_bond = 1\n",
    "valence = 4\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2105,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#with tf.device('/gpu:0'):\n",
    "if 1==1:\n",
    "    x = tf.placeholder(tf.float32, [S, B, L_list[0]], name=\"atom_features\")\n",
    "    keep_prob = tf.placeholder(tf.float32, name=\"keep_prob\")\n",
    "    \n",
    "    adj_matrix = tf.placeholder(tf.float32, shape=[S, B, B], name=\"adj_matrix\")\n",
    "    \n",
    "    non_adj_matrix = tf.subtract(tf.ones_like(adj_matrix), adj_matrix)\n",
    "    \n",
    "    mol_xyz = tf.placeholder(tf.float32, shape=[S, B, 3], name=\"molxyz\")\n",
    "    \n",
    "    z = tf.random_uniform([S, D, z_dim], -1, 1)\n",
    "    zg = tf.random_uniform([S, D], -2, 2)\n",
    "    \n",
    "    mol_noise = tf.random_normal([S,D,1], mean=0,stddev=0.01)\n",
    "    \n",
    "    dihed_indices = tf.placeholder(tf.float32, shape=[S, D, B, 4])\n",
    "    dihed_tensor = tf.placeholder(tf.float32, shape=[S, D, 1])\n",
    "    rot_vectors = tf.placeholder(tf.float32, shape=[S, D, 3])\n",
    "    atom_index_of_bond = tf.placeholder(tf.float32, shape=[S, D, B])\n",
    "    subgraph = tf.placeholder(tf.float32, shape=[S, D, B, B])\n",
    "    \n",
    "    phase = tf.placeholder(tf.bool)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2106,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#adapted from: http://stackoverflow.com/questions/37009647/compute-pairwise-distance-in-a-batch-without-replicating-tensor-in-tensorflow\n",
    "def compute_distance_matrix(A):\n",
    "    r = tf.reduce_sum(A*A, 1) # turn r into column vector \n",
    "    r = tf.reshape(r, [-1, 1]) \n",
    "    D = r - 2*tf.matmul(A, tf.transpose(A)) + tf.transpose(r)\n",
    "    return(D)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2107,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "building clades and adj\n",
      "Building hidden layers\n"
     ]
    }
   ],
   "source": [
    "GW_list = [None for i in range(n_layers)]\n",
    "Gb_list = [None for i in range(n_layers)]\n",
    "\n",
    "DW_list = [None for i in range(n_layers)]\n",
    "Db_list = [None for i in range(n_layers)]\n",
    "\n",
    "\n",
    "Gh_list = [x]\n",
    "Dh_list = [x]\n",
    "\n",
    "adj_list = [adj_matrix]\n",
    "B_list = [B]\n",
    "clades_list = []\n",
    "graph_stride = 1.\n",
    "\n",
    "#with tf.device('/gpu:0'):\n",
    "if 1==1:\n",
    "\n",
    "    print(\"building clades and adj\")\n",
    "    for i in range(n_layers):\n",
    "        print(i)\n",
    "        B_list.append(int(np.ceil(B_list[i]/graph_stride)))\n",
    "        print(B_list)\n",
    "        clades_list.append(tf.stack([tf.one_hot(range(0,B_list[i],int(graph_stride)), depth=B_list[i])]*S, axis=0)) \n",
    "\n",
    "\n",
    "        adj_temp = tf.matmul(clades_list[i], adj_list[i])\n",
    "        sub_adj = tf.matmul(adj_temp, tf.transpose(adj_temp, perm=[0, 2, 1]))\n",
    "        sub_adj = tf.minimum(sub_adj, 1.)\n",
    "        adj_list.append(sub_adj)\n",
    "\n",
    "    print(\"Building hidden layers\")\n",
    "    for layer_idx in range(n_layers):\n",
    "        GW_list[layer_idx] = tf.Variable(tf.truncated_normal([L_list[layer_idx], L_list[layer_idx+1]], seed=2017), name=\"GW_list%d\" %layer_idx)\n",
    "        Gb_list[layer_idx] = tf.Variable(tf.ones([1, L_list[layer_idx+1]]))\n",
    "\n",
    "        DW_list[layer_idx] = tf.Variable(tf.truncated_normal([L_list[layer_idx], L_list[layer_idx+1]], seed=2017), name=\"DW_list%d\" %layer_idx)\n",
    "        Db_list[layer_idx] = tf.Variable(tf.ones([1, L_list[layer_idx+1]]))\n",
    "\n",
    "    f_dw1 = tf.Variable(tf.truncated_normal([L_list[n_layers], L_list[n_layers]]), name=\"f_dw1\")\n",
    "    f_db1 = tf.Variable(tf.ones([L_list[n_layers]]))\n",
    "\n",
    "    f_gw1 = tf.Variable(tf.truncated_normal([L_list[n_layers], L_list[n_layers]]))\n",
    "    f_gb1 = tf.Variable(tf.ones([L_list[n_layers]]))\n",
    "\n",
    "    g_w1_ini = tf.Variable(tf.truncated_normal([1, L_list[n_layers]*4+z_dim, 64], stddev=1.))\n",
    "    g_w1 = tf.tile(g_w1_ini, [S, 1, 1])\n",
    "    g_g1 = tf.Variable(tf.ones([1,1,64]))\n",
    "\n",
    "    g_b1 = tf.Variable(tf.ones([1, 1, 64]))\n",
    "\n",
    "    g_w2_ini = tf.Variable(tf.truncated_normal([1, 64, 64], stddev=1.))\n",
    "    g_w2 = tf.tile(g_w2_ini, [S, 1, 1])\n",
    "    g_g2 = tf.Variable(tf.ones([1,1,64]))\n",
    "\n",
    "    g_b2 = tf.Variable(tf.ones([1, 1, 64]))\n",
    "\n",
    "    g_w3_ini = tf.Variable(tf.truncated_normal([1, 64, 1], stddev=1.))\n",
    "    g_w3 = tf.tile(g_w3_ini, [S, 1, 1])\n",
    "    g_b3 = tf.Variable(tf.ones([1, 1, 1]))\n",
    "    g_g3 = tf.Variable(tf.ones([1,1,1]))\n",
    "\n",
    "    dd_w1_ini = tf.Variable(tf.truncated_normal([1, L_list[n_layers]*4+1, 32]))\n",
    "    dd_w1 = tf.tile(dd_w1_ini, [S, 1, 1])\n",
    "    dd_b1 = tf.Variable(tf.truncated_normal([1, 1, 32]))\n",
    "\n",
    "    dd_w2_ini = tf.Variable(tf.truncated_normal([1,32, 32]))\n",
    "    dd_w2 = tf.tile(dd_w2_ini, [S, 1, 1])\n",
    "    dd_b2 = tf.Variable(tf.truncated_normal([1,1,32]))\n",
    "\n",
    "    dd_w3_ini = tf.Variable(tf.truncated_normal([1,32, 1]))\n",
    "    dd_w3 = tf.tile(dd_w2_ini, [S, 1, 1])\n",
    "    dd_b3 = tf.Variable(tf.truncated_normal([1,1,1]))\n",
    "    \n",
    "    \n",
    "    d_w1_ini = tf.Variable(tf.truncated_normal([1, B*(L_list[n_layers]*2+1), 32]))\n",
    "    d_w1 = tf.tile(d_w1_ini, [S, 1, 1])\n",
    "    d_b1 = tf.Variable(tf.ones([1, 1, 32]))\n",
    "\n",
    "    d_w2_ini = tf.Variable(tf.truncated_normal([1, 32, 1]))\n",
    "    d_w2 = tf.tile(d_w2_ini, [S, 1, 1])\n",
    "    d_b2 = tf.Variable(tf.ones([1, 1, 1]))\n",
    "\n",
    "    lam1 = tf.Variable(tf.constant(0.01))\n",
    "    lam2 = tf.Variable(tf.constant(0.01))\n",
    "    lam3 = tf.Variable(tf.constant(0.01))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2108,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def adjacency_conv_layer(x, W, b, adj, clades, L_in, L_out, layer_idx, S, B):\n",
    "    with tf.device('/gpu:0'):\n",
    "        print(\"layer_idx: %d\" %(layer_idx))\n",
    "        h = tf.matmul(adj, x, name=\"adj_mult_%d\" %layer_idx)\n",
    "        h = tf.reshape(h, shape=(S*B, L_in), name=\"adj_reshape_1_%d\" %layer_idx)\n",
    "\n",
    "        h = tf.matmul(h, W, name=\"adjconv_%d\" %layer_idx) + b\n",
    "    \n",
    "        h = tf.nn.tanh(h)\n",
    "        h = tf.reshape(h, (S, B, L_out), name=\"adj_reshape_2_%d\" %layer_idx)  \n",
    "\n",
    "        h = tf.matmul(clades, h)\n",
    "\n",
    "        print(\"within func h:\")\n",
    "        print(h)                                                                                                                              \n",
    "\n",
    "    return(h)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2109,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#t = sess.run(tf.matmul(dihed_indices[:,:,:,0], x))\n",
    "#t = np.reshape(t, [t.shape[0]*t.shape[1],t.shape[2]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2110,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'\\ndef compute_dihedral_tensor(mols_batch, angle_tuples_batch):\\n    i_vec = tf.reshape(mols_batch, [S, B, 1, 3])\\n    i_vec = tf.tile(i_vec, [1, 1, B, 1])\\n\\n    j_vec = tf.reshape(mols_batch, [S, 1, B, 3])\\n    j_vec = tf.tile(j_vec, [1, B, 1, 1])\\n\\n    diff = tf.subtract(i_vec, j_vec)\\n    temp = tf.eye(B, batch_shape=[S])\\n    temp = tf.reshape(temp, [S, B, B, 1])\\n    temp = tf.tile(temp, [1, 1, 1, 3])\\n    diff = diff + temp\\n    diff = diff / tf.sqrt(tf.reduce_sum(tf.square(diff), axis=3, keep_dims=True))\\n    temp = tf.sqrt(tf.reduce_sum(tf.square(temp), axis=3, keep_dims=True))\\n    diff = tf.subtract(diff, temp)\\n\\n    print(\"diff should be [S, B, B, 3]\")\\n    print(diff)\\n\\n    ij = tf.tile(tf.reshape(diff, [S, B, 1, B, 3]), [1, 1, B, 1, 1])\\n    ik = tf.tile(tf.reshape(diff, [S, B, B, 1, 3]), [1, 1, 1, B, 1])\\n    cross = tf.cross(ij, ik)\\n    cross = cross / tf.sqrt(tf.reduce_sum(tf.square(cross), axis=4, keep_dims=True))\\n    \\n    ijk = tf.tile(tf.reshape(cross, [S, B, B, 1, B, 3]), [1m 1])\\n    \\n    dps = tf.reduce_sum(tf.multiply(ij ,ik), axis=4)\\n    #dps = tf.where(tf.is_nan(dps), tf.ones_like(dps) * 0., dps)\\n    print(\"dps should be [S, B, B, B]\")\\n    print(dps)\\n\\n    #angles_gen = tf.acos(tf.clip_by_value(dps, -1.0, 1.0))\\n    angles_computed = tf.where(tf.is_nan(dps), tf.zeros_like(dps), dps)\\n    angles_computed = tf.multiply(angles_computed, angle_tuples_batch)\\n    return(angles_computed)\\n'"
      ]
     },
     "execution_count": 2110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def compute_angle_tensor(mols_batch, angle_tuples_batch):\n",
    "    i_vec = tf.reshape(mols_batch, [S, B, 1, 3])\n",
    "    i_vec = tf.tile(i_vec, [1, 1, B, 1])\n",
    "\n",
    "    j_vec = tf.reshape(mols_batch, [S, 1, B, 3])\n",
    "    j_vec = tf.tile(j_vec, [1, B, 1, 1])\n",
    "\n",
    "    diff = tf.subtract(i_vec, j_vec)\n",
    "    temp = tf.eye(B, batch_shape=[S])\n",
    "    temp = tf.reshape(temp, [S, B, B, 1])\n",
    "    temp = tf.tile(temp, [1, 1, 1, 3])\n",
    "    diff = diff + temp\n",
    "    diff = diff / tf.sqrt(tf.reduce_sum(tf.square(diff), axis=3, keep_dims=True))\n",
    "    temp = tf.sqrt(tf.reduce_sum(tf.square(temp), axis=3, keep_dims=True))\n",
    "    diff = tf.subtract(diff, temp)\n",
    "\n",
    "    print(\"diff should be [S, B, B, 3]\")\n",
    "    print(diff)\n",
    "\n",
    "    ij = tf.tile(tf.reshape(diff, [S, B, 1, B, 3]), [1, 1, B, 1, 1])\n",
    "    ik = tf.tile(tf.reshape(diff, [S, B, B, 1, 3]), [1, 1, 1, B, 1])\n",
    "    dps = tf.reduce_sum(tf.multiply(ij ,ik), axis=4)\n",
    "    #dps = tf.where(tf.is_nan(dps), tf.ones_like(dps) * 0., dps)\n",
    "    print(\"dps should be [S, B, B, B]\")\n",
    "    print(dps)\n",
    "\n",
    "    #angles_gen = tf.acos(tf.clip_by_value(dps, -1.0, 1.0))\n",
    "    angles_computed = tf.where(tf.is_nan(dps), tf.zeros_like(dps), dps)\n",
    "    angles_computed = tf.multiply(angles_computed, angle_tuples_batch)\n",
    "    return(angles_computed)\n",
    "\n",
    "def compute_dist_tensor(mols_batch):\n",
    "    dist_matrix = tf.concat([tf.reshape(compute_distance_matrix(tf.reshape(mol, [B, 3])), (1, B, B)) for mol in tf.split(mols_batch, S, axis=0)], axis=0)\n",
    "    #dist_matrix = tf.where(tf.is_nan(dist_matrix), tf.ones_like(dist_matrix) * 0., dist_matrix)\n",
    "    print(\"dist_matrix\")\n",
    "    print(dist_matrix)\n",
    "    #dist_matrix = compute_distance_matrix(mol)\n",
    "    return(dist_matrix)\n",
    "\n",
    "def compute_rot_matrix(t, vec):\n",
    "    x, y, z = vec[0], vec[1], vec[2]\n",
    "    rot_matrix = tf.reshape(tf.stack([tf.cos(t) + tf.square(x) * (1 - tf.cos(t)), \n",
    "                               x*y*(1-tf.cos(t)) - z*tf.sin(t),\n",
    "                               x*z*(1-tf.cos(t)) + y*tf.sin(t),\n",
    "                              y*x*(1-tf.cos(t)) + z*tf.sin(t),\n",
    "                               tf.cos(t) + y*y*(1-tf.cos(t)),\n",
    "                               y*z*(1-tf.cos(t)) - x*tf.sin(t),\n",
    "                              z*x*(1-tf.cos(t)) - y*tf.sin(t),\n",
    "                               z*y*(1-tf.cos(t)) + x*tf.sin(t),\n",
    "                               tf.cos(t)+ z*z*(1-tf.cos(t))]), (3,3))\n",
    "    return(rot_matrix)\n",
    "\n",
    "\"\"\"\n",
    "def compute_dihedral_tensor(mols_batch, angle_tuples_batch):\n",
    "    i_vec = tf.reshape(mols_batch, [S, B, 1, 3])\n",
    "    i_vec = tf.tile(i_vec, [1, 1, B, 1])\n",
    "\n",
    "    j_vec = tf.reshape(mols_batch, [S, 1, B, 3])\n",
    "    j_vec = tf.tile(j_vec, [1, B, 1, 1])\n",
    "\n",
    "    diff = tf.subtract(i_vec, j_vec)\n",
    "    temp = tf.eye(B, batch_shape=[S])\n",
    "    temp = tf.reshape(temp, [S, B, B, 1])\n",
    "    temp = tf.tile(temp, [1, 1, 1, 3])\n",
    "    diff = diff + temp\n",
    "    diff = diff / tf.sqrt(tf.reduce_sum(tf.square(diff), axis=3, keep_dims=True))\n",
    "    temp = tf.sqrt(tf.reduce_sum(tf.square(temp), axis=3, keep_dims=True))\n",
    "    diff = tf.subtract(diff, temp)\n",
    "\n",
    "    print(\"diff should be [S, B, B, 3]\")\n",
    "    print(diff)\n",
    "\n",
    "    ij = tf.tile(tf.reshape(diff, [S, B, 1, B, 3]), [1, 1, B, 1, 1])\n",
    "    ik = tf.tile(tf.reshape(diff, [S, B, B, 1, 3]), [1, 1, 1, B, 1])\n",
    "    cross = tf.cross(ij, ik)\n",
    "    cross = cross / tf.sqrt(tf.reduce_sum(tf.square(cross), axis=4, keep_dims=True))\n",
    "    \n",
    "    ijk = tf.tile(tf.reshape(cross, [S, B, B, 1, B, 3]), [1m 1])\n",
    "    \n",
    "    dps = tf.reduce_sum(tf.multiply(ij ,ik), axis=4)\n",
    "    #dps = tf.where(tf.is_nan(dps), tf.ones_like(dps) * 0., dps)\n",
    "    print(\"dps should be [S, B, B, B]\")\n",
    "    print(dps)\n",
    "\n",
    "    #angles_gen = tf.acos(tf.clip_by_value(dps, -1.0, 1.0))\n",
    "    angles_computed = tf.where(tf.is_nan(dps), tf.zeros_like(dps), dps)\n",
    "    angles_computed = tf.multiply(angles_computed, angle_tuples_batch)\n",
    "    return(angles_computed)\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2111,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def wnorm_layer(w, x, gamma, alpha):\n",
    "    axes = range(0, len(w.get_shape()))\n",
    "    wn = tf.nn.l2_normalize(w, -1)*gamma\n",
    "    y = tf.nn.tanh(tf.matmul(x, wn) - alpha) + alpha\n",
    "    return(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2112,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def generator(W_list, b_list, h_list, L_list, n_layers, mols):\n",
    "    if 1==1:\n",
    "    #with tf.variable_scope(\"generator\"):\n",
    "        for layer_idx in range(n_layers):\n",
    "            h_list.append(tf.nn.dropout(adjacency_conv_layer(h_list[layer_idx], W_list[layer_idx],\n",
    "                                                         b_list[layer_idx], adj_list[layer_idx], \n",
    "                                                         clades_list[layer_idx],\n",
    "                                                         L_list[layer_idx], L_list[layer_idx+1],\n",
    "                                                         layer_idx, S, B_list[layer_idx]), keep_prob))\n",
    "        L_final = L_list[n_layers]  \n",
    "\n",
    "        print(\"h_list[-1]\")\n",
    "        print(h_list[-1])\n",
    "\n",
    "        print(\"B_list\")\n",
    "        print(B_list)\n",
    "\n",
    "        fingerprints = tf.tile(tf.reshape(tf.nn.tanh(tf.matmul(tf.reduce_sum(h_list[-1], axis=1), f_gw1) + f_gb1), [S, 1, L_final]), [1, D, 1])\n",
    "\n",
    "        #fingerprints = tf.tile(tf.reshape(tf.layers.batch_normalization(tf.nn.tanh(tf.matmul(tf.reduce_sum(h_list[-1], axis=1), f_gw1) + f_gb1), training=phase, name=\"gbn1\"), [S, 1, L_final]), [1, D, 1])\n",
    "        h_final = h_list[-1]\n",
    "\n",
    "        d0 = []\n",
    "        for l in range(0, valence*dihed_per_bond):\n",
    "            d0.append(tf.matmul(dihed_indices[:,:,:,l], h_final, name=\"dihed_mult\"))\n",
    "\n",
    "        d0 = tf.concat(d0, axis=2)\n",
    "        print(\"d0 should be shape %s\" %(str((S, D, L_list[n_layers]*4))))\n",
    "        d0 = tf.concat([d0, z], axis=2)\n",
    "\n",
    "        #PREDICT ANGLES:\n",
    "        g_h1 = tf.nn.dropout(tf.nn.tanh(tf.matmul(d0, g_w1) + g_b1), keep_prob)\n",
    "        #g_h1 = wnorm_layer(g_w1, d0, g_g1, g_b1)\n",
    "        #g_h1 = tf.nn.tanh(tf.matmul(d0, wnorm(g_w1, g_g1)) + g_b1)\n",
    "        #g_h1 = tf.reshape(g_h1, [S, 5, 16])\n",
    "        print(\"g_h1\")\n",
    "        print(g_h1)\n",
    "        #g_h2 = tf.nn.dropout(tf.nn.tanh(tf.matmul(g_h1, g_w2) + g_b2), 1.)\n",
    "        g_h2 = tf.nn.dropout(tf.nn.tanh(tf.matmul(g_h1, g_w2)+g_b2), keep_prob)\n",
    "        \n",
    "        print(\"g_h2\")\n",
    "        print(g_h2)\n",
    "        \n",
    "        #g_h2 = wnorm_layer(g_w2, g_h1, g_g2, g_b2)\n",
    "        \n",
    "        g_h3 = tf.reshape(tf.matmul(g_h2, g_w3) + g_b3, [S, D, 1])\n",
    "        #g_h3 = wnorm_layer(g_w3, g_h2, g_g3, g_b3)\n",
    "        \n",
    "        print(\"g_h3\")\n",
    "        print(g_h3)\n",
    "        #g_h3 = tf.atan2(tf.sin(g_h3), tf.cos(g_h3))\n",
    "\n",
    "        output = g_h3\n",
    "        \"\"\"\n",
    "        def sample_gen_cdf(params, noise):\n",
    "            angle = 0.\n",
    "            for i in range(0,3):\n",
    "                comp = tf.nn.tanh(tf.add(tf.multiply(params[:,:,i*3+1], noise), params[:,:,i*3+2]))\n",
    "                print(\"comp1\")\n",
    "                print(comp)\n",
    "                comp = tf.multiply(params[:,:,i*3], comp)\n",
    "                print(\"comp2\")\n",
    "                print(comp)\n",
    "                angle = tf.add(angle, comp)\n",
    "            return(angle)\n",
    "        \n",
    "        output = sample_gen_cdf(g_h3, zg)\n",
    "        \"\"\"\n",
    "\n",
    "\n",
    "        #ROTATE MOLECULE: \n",
    "        #for i in range(n_bonds):\n",
    "            #rotation point: atom_index_of_bond * mol_xyz\n",
    "            #\n",
    "            #rot_mat = compute_rotation_matrix(old_angle - g_h2, axis)\n",
    "            #new_xyz = rot_mat*subgraph*xyz + (1-subgraph)*xyz\n",
    "        \n",
    "        \"\"\"\n",
    "        new_mols = []\n",
    "        for i in range(0,S):\n",
    "            mol = mol_xyz[i]\n",
    "            for j in range(0,D):\n",
    "                rot_point = tf.matmul(tf.reshape(atom_index_of_bond[i,j,:], (1, B)), mol)\n",
    "                mol = tf.subtract(mol, rot_point)\n",
    "                rot_mat = compute_rot_matrix(tf.subtract(g_h3[i, j], dihed_tensor[i, j]), rot_vectors[i, j])\n",
    "                if i ==0 and j ==0:\n",
    "                    rot_mat_i = rot_mat\n",
    "\n",
    "                frag_i = tf.transpose(tf.matmul(subgraph[i, j, :, :], mol), perm=[1,0])\n",
    "                new_xyz = tf.transpose(tf.matmul(rot_mat, frag_i), perm=[1,0])\n",
    "\n",
    "                other_subgraph = tf.subtract(tf.eye(B), subgraph[i, j, :, :])\n",
    "                mol = tf.add(new_xyz, tf.matmul(other_subgraph, mol))\n",
    "            new_mols.append(mol)\n",
    "        new_mol = tf.stack(new_mols, axis=0)\n",
    "        \"\"\"\n",
    "\n",
    "        \"\"\"\n",
    "        for i in range(D):\n",
    "            rot_point = tf.matmul(tf.reshape(atom_index_of_bond[:,i,:], (S, 1, B)), mol_xyz)\n",
    "            tf.subtract(mol_xyz, rot_point)\n",
    "            rot_mat = []\n",
    "            for j in range(0,S):\n",
    "                rot_mat.append(tf.reshape(compute_rot_matrix(tf.subtract(g_h3[j, i], dihed_tensor[j, i]), rot_vectors[j, i]), [1, 3, 3]))\n",
    "            rot_mat = tf.concat(rot_mat, axis=0)\n",
    "            if i == 0:\n",
    "                rot_mat_i = rot_mat\n",
    "\n",
    "            frag_i = tf.transpose(tf.matmul(subgraph[:, i, :, :], mol_xyz), perm=[0,2,1])\n",
    "            new_xyz = tf.transpose(tf.matmul(rot_mat, frag_i), perm=[0,2,1])\n",
    "\n",
    "            other_subgraph = tf.subtract(tf.tile(tf.reshape(tf.eye(B), [1, B, B]), [S, 1, 1]), subgraph[:, i, :, :])\n",
    "            new_xyz = tf.add(new_xyz, tf.matmul(other_subgraph, mol_xyz))\n",
    "        \"\"\"\n",
    "        return(mols, output, tf.ones([3,3]))\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2113,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def Normal(mu, sigma, a):\n",
    "    return(tf.sqrt(1/(2*3.14159*(sigma**2))) * tf.exp(-((a-mu)**2) / (2*sigma**2)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2114,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def discriminator(W_list, b_list, h_list, L_list, n_layers, mol_real, dihed, reuse=False):\n",
    "    with tf.variable_scope(\"discriminator\"):\n",
    "\n",
    "        #with tf.device('/gpu:0'):\n",
    "        if 1==1:\n",
    "            for layer_idx in range(n_layers):\n",
    "                h_list.append(tf.nn.dropout(adjacency_conv_layer(h_list[layer_idx], W_list[layer_idx],\n",
    "                                                 b_list[layer_idx], adj_list[layer_idx], \n",
    "                                                 clades_list[layer_idx],\n",
    "                                                 L_list[layer_idx], L_list[layer_idx+1],\n",
    "                                                 layer_idx, S, B_list[layer_idx]), keep_prob))        \n",
    "            #DIHEDRAL LOSS:\n",
    "            fingerprints = tf.tile(tf.reshape(tf.nn.tanh(tf.matmul(tf.reduce_sum(h_list[-1], axis=1), f_dw1) + f_db1), [S, 1, L_list[n_layers]]), [1, D, 1])\n",
    "            #fingerprints = tf.tile(tf.reshape(tf.layers.batch_normalization(tf.nn.tanh(tf.matmul(tf.reduce_sum(h_list[-1], axis=1), f_dw1) + f_db1), training=phase, name=\"dbn1\", reuse=reuse), [S, 1, L_list[n_layers]]), [1, D, 1])\n",
    "            h_final = h_list[-1]\n",
    "\n",
    "            d0 = []\n",
    "            for l in range(0, valence*dihed_per_bond):\n",
    "                d0.append(tf.matmul(dihed_indices[:,:,:,l], h_final, name=\"dihed_mult\"))\n",
    "\n",
    "            d0 = tf.concat(d0, axis=2)\n",
    "            print(\"d0 should be shape %s\" %(str((S, D, L_list[n_layers]*4+1))))\n",
    "            print(d0)\n",
    "            d0 = tf.concat([d0, dihed], axis=2)\n",
    "            d0 = tf.reshape(d0, [S, D, L_list[n_layers]*4 + 1])\n",
    "            \n",
    "            d1 = tf.nn.dropout(tf.nn.tanh(tf.matmul(d0, dd_w1) + dd_b1), 1.)\n",
    "            #d1 = tf.layers.batch_normalization(tf.nn.tanh(tf.matmul(d0, dd_w1) + dd_b1), training=phase, name=\"dbn2\", reuse=reuse, scale=True)\n",
    "            d2 = tf.nn.tanh(tf.matmul(d1, dd_w2) + dd_b2)\n",
    "            d3 = tf.reduce_mean(tf.matmul(d2, dd_w3) + dd_b3, axis=1, keep_dims=True)\n",
    "            return(d3)\n",
    "            \"\"\"\n",
    "            \n",
    "            #prob = tf.nn.sigmoid(d2)\n",
    "            \n",
    "            print(\"d2\")\n",
    "            print(d2)\n",
    "            \n",
    "            prob = 0.\n",
    "            for i in range(0,3):\n",
    "                prob += d2[:, :, i*3] * Normal(mu=d2[:, :, i*3+1], sigma=d2[:, :, i*3+2], a=tf.reshape(dihed, (S,D)))\n",
    "            \n",
    "            return(prob)\n",
    "            \"\"\"\n",
    "            \n",
    "            #prob = tf.reduce_mean(tf.reshape(prob, [S, D]), axis=1, keep_dims=True)\n",
    "            #prob = tf.nn.tanh(tf.reduce_mean(prob, axis=1, keep_dims=True))\n",
    "\n",
    "\n",
    "            #COMPUTE CONTACT LOSS:\n",
    "            \"\"\"\n",
    "            new_mols = []\n",
    "            for i in range(0,S):\n",
    "                mol = mol_real[i]\n",
    "                for j in range(0,D):\n",
    "                    rot_point = tf.matmul(tf.reshape(atom_index_of_bond[i,j,:], (1, B)), mol)\n",
    "                    mol = tf.subtract(mol, rot_point)\n",
    "                    rot_mat = compute_rot_matrix(tf.subtract(dihed[i, j], dihed_tensor[i, j]), rot_vectors[i, j])\n",
    "                    if i ==0 and j ==0:\n",
    "                        rot_mat_i = rot_mat\n",
    "\n",
    "                    frag_i = tf.transpose(tf.matmul(subgraph[i, j, :, :], mol), perm=[1,0])\n",
    "                    new_xyz = tf.transpose(tf.matmul(rot_mat, frag_i), perm=[1,0])\n",
    "\n",
    "                    other_subgraph = tf.subtract(tf.eye(B), subgraph[i, j, :, :])\n",
    "                    mol = tf.add(new_xyz, tf.matmul(other_subgraph, mol))\n",
    "                new_mols.append(mol)\n",
    "            mol_gen = tf.stack(new_mols, axis=0)\n",
    "\n",
    "\n",
    "            dist_real = compute_dist_tensor(mol_real)\n",
    "            dist_fake = compute_dist_tensor(mol_gen)\n",
    "\n",
    "            dist_loss = tf.multiply(tf.reduce_sum(tf.squared_difference(dist_fake, dist_real), axis=[1,2]), lam1)\n",
    "            \"\"\"\n",
    "\n",
    "            \"\"\"\n",
    "            contact_dist = tf.multiply(dist_matrix_gen, non_adj_matrix)\n",
    "            contact_loss = tf.multiply(tf.reshape(tf.reduce_mean(tf.exp(tf.multiply(tf.multiply(tf.subtract(contact_dist, 2.), -1.), 3.)), axis=[1,2]), (S,1)), lam3)        \n",
    "\n",
    "            feat_i = tf.reshape(h_final, (S, B, 1, L_list[n_layers]))\n",
    "            feat_i = tf.tile(feat_i, [1, 1, B, 1])\n",
    "\n",
    "            feat_j = tf.reshape(h_final, (S, B, 1, L_list[n_layers]))\n",
    "            feat_j = tf.transpose(feat_j, perm=[0, 2, 1, 3])\n",
    "            feat_j = tf.tile(feat_j, [1, B, 1, 1])\n",
    "\n",
    "            d_h1 = tf.concat([tf.reshape(dist_matrix_gen, [S, B, B, 1]), feat_i, feat_j], axis=3)\n",
    "            d_h1 = tf.reshape(d_h1, [S, B, B*(L_list[n_layers]*2+1)])\n",
    "            d_h2 = tf.nn.dropout(tf.nn.tanh(tf.matmul(d_h1, d_w1) + d_b1), keep_prob)\n",
    "            d_h2 = tf.divide(tf.reduce_sum(tf.matmul(d_h2, d_w2) + d_b2, axis=1), n_bonds)#, axis=1\n",
    "            print(\"d_h2\")\n",
    "            print(d_h2)\n",
    "            \"\"\"\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2115,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "theta_D = DW_list[:n_layers] + Db_list[:n_layers] + [d_w1_ini, d_b1, d_w2_ini, d_b2, dd_w1_ini, dd_b1, dd_w2_ini, dd_b2, dd_w3_ini, dd_b3, f_dw1, f_db1, lam1] + [n for n in tf.trainable_variables() if \"dbn\" in str(n)]\n",
    "theta_G = GW_list[:n_layers] + Gb_list[:n_layers] + [g_w1_ini, g_b1, g_w2_ini, g_b2, g_w3_ini, g_b3, f_gw1, f_gb1, g_g1, g_g2, g_g3] + [n for n in tf.trainable_variables() if \"gbn\" in str(n)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2116,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "h_list[-1]\n",
      "Tensor(\"atom_features:0\", shape=(32, 48, 75), dtype=float32)\n",
      "B_list\n",
      "[48]\n",
      "d0 should be shape (32, 16, 300)\n",
      "g_h1\n",
      "Tensor(\"dropout/mul:0\", shape=(32, 16, 64), dtype=float32)\n",
      "g_h2\n",
      "Tensor(\"dropout_1/mul:0\", shape=(32, 16, 64), dtype=float32)\n",
      "g_h3\n",
      "Tensor(\"Reshape_1:0\", shape=(32, 16, 1), dtype=float32)\n",
      "dihed, returned\n",
      "Tensor(\"Reshape_1:0\", shape=(32, 16, 1), dtype=float32)\n",
      "d0 should be shape (32, 16, 301)\n",
      "Tensor(\"discriminator/concat:0\", shape=(32, 16, 300), dtype=float32)\n",
      "d0 should be shape (32, 16, 301)\n",
      "Tensor(\"discriminator_1/concat:0\", shape=(32, 16, 300), dtype=float32)\n"
     ]
    }
   ],
   "source": [
    "import time \n",
    "\n",
    "#with tf.device('/gpu:0'):\n",
    "if 1==1:\n",
    "\n",
    "    G_sample, dihed, rot_mat_i = generator(GW_list, Gb_list, Gh_list, L_list, n_layers, mol_xyz)\n",
    "    print(\"dihed, returned\")\n",
    "    print(dihed)\n",
    "    #dihed = tf.clip_by_value(dihed, -3.2, 3.2)\n",
    "    \n",
    "    D_real = discriminator(DW_list, Db_list, Dh_list, L_list, n_layers, mol_xyz, dihed_tensor + mol_noise, reuse=False)\n",
    "    D_fake = discriminator(DW_list, Db_list, Dh_list, L_list, n_layers, mol_xyz, dihed, reuse=True)\n",
    "\n",
    "    #D_loss = tf.reduce_mean(D_real) - tf.reduce_mean(D_fake)\n",
    "    #G_loss = -tf.reduce_mean(D_fake)\n",
    "    \n",
    "    D_loss = tf.reduce_sum(tf.square(D_real-1) + tf.square(D_fake))/2\n",
    "    G_loss = tf.reduce_sum(tf.square(D_fake-1))/2\n",
    "    \n",
    "    D_solver = (tf.train.RMSPropOptimizer(learning_rate=5e-4)\n",
    "                .minimize(D_loss, var_list=theta_D))\n",
    "    G_solver = (tf.train.RMSPropOptimizer(learning_rate=5e-4)\n",
    "                .minimize(G_loss, var_list=theta_G))\n",
    "    \n",
    "    d_ops = [op for op in tf.get_collection(tf.GraphKeys.UPDATE_OPS) if \"dbn\" in str(op)]\n",
    "    g_ops = [op for op in tf.get_collection(tf.GraphKeys.UPDATE_OPS) if \"gbn\" in str(op)]\n",
    "\n",
    "    \n",
    "    \n",
    "    \n",
    "    # WGAN lipschitz-penalty\n",
    "    \"\"\"\n",
    "    alpha = tf.random_uniform(\n",
    "        shape=[S,1,1], \n",
    "        minval=0.,\n",
    "        maxval=1.\n",
    "    )\n",
    "    differences = dihed_tensor - dihed\n",
    "    interpolates = dihed_tensor + (alpha*differences)\n",
    "    print(\"interpolates\")\n",
    "    print(interpolates)\n",
    "    \n",
    "    gradients = tf.gradients(discriminator(DW_list, Db_list, Dh_list, L_list, n_layers, mol_xyz, interpolates), [interpolates])[0]\n",
    "    print(\"Gradients\")\n",
    "    print(gradients)\n",
    "    slopes = tf.sqrt(tf.reduce_sum(tf.square(gradients), reduction_indices=[1,2]))\n",
    "    gradient_penalty = tf.reduce_mean((slopes-1.)**2)\n",
    "    LAMBDA = 10\n",
    "    D_loss += LAMBDA*gradient_penalty\n",
    "    \n",
    "    G_solver = tf.train.AdamOptimizer(learning_rate=1e-3, beta1=0.5, beta2=0.9).minimize(G_loss, var_list=gen_params)\n",
    "    D_solver = tf.train.AdamOptimizer(learning_rate=1e-3, beta1=0.5, beta2=0.9).minimize(D_loss, var_list=disc_params)\n",
    "    \"\"\"\n",
    "\n",
    "    \"\"\"\n",
    "    clip_D = []\n",
    "    for p in theta_D[:-1]:\n",
    "        #print(p)\n",
    "        clip_D.append(p.assign(tf.clip_by_value(p, -0.01, 0.01)))\n",
    "    clip_D.append(lam1.assign(tf.clip_by_value(lam1, 0.001, 0.01)))\n",
    "\n",
    "    \n",
    "    D_solver = (tf.train.RMSPropOptimizer(learning_rate=5e-4)\n",
    "                .minimize(-D_loss, var_list=theta_D))\n",
    "    G_solver = (tf.train.RMSPropOptimizer(learning_rate=5e-4)\n",
    "                .minimize(G_loss, var_list=theta_G))\n",
    "    \"\"\"\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2117,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 2117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tf.get_collection(tf.GraphKeys.UPDATE_OPS)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2118,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "mb_size = S\n",
    "\n",
    "sess = tf.Session(config=tf.ConfigProto(log_device_placement=True))\n",
    "sess.run(tf.global_variables_initializer())\n",
    "\n",
    "\n",
    "preds = []\n",
    "\n",
    "d_losses = []\n",
    "g_losses = []\n",
    "#print(sess.run(label_placeholder))\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2119,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "def construct_feed_dict(X, start=None,\n",
    "                      stop=None, y=None,\n",
    "                      keep_prob_val=1., train=True,\n",
    "                       shuffle_inds=True):\n",
    "    a = time.time()\n",
    "    if start is None:\n",
    "      start = 0\n",
    "      stop = len(X)\n",
    "    \n",
    "    inds = range(start, stop)\n",
    "    if shuffle_inds:\n",
    "        random.shuffle(inds)\n",
    "\n",
    "    atom_adj_batch = [X[idx][7][0] for idx in inds]\n",
    "    \n",
    "    A_batch = np.array([X[idx][7][1] for idx in inds])\n",
    "    mol_xyz_batch = [X[idx][1] for idx in inds]\n",
    "    dihed_tuples_batch = [X[idx][2] for idx in inds]\n",
    "    dihed_tensor_batch = [X[idx][3] for idx in inds]\n",
    "    subgraph_batch = [X[idx][4] for idx in inds]\n",
    "    atom_index_of_bond_batch = [X[idx][5] for idx in inds]\n",
    "    rot_vectors_batch = [X[idx][6] for idx in inds]\n",
    "\n",
    "    feed_dict = {x: A_batch,\n",
    "                 adj_matrix: atom_adj_batch,\n",
    "                 mol_xyz: mol_xyz_batch,\n",
    "                 dihed_indices: dihed_tuples_batch,\n",
    "                 dihed_tensor: dihed_tensor_batch,\n",
    "                 subgraph: subgraph_batch,\n",
    "                 atom_index_of_bond: atom_index_of_bond_batch,\n",
    "                 rot_vectors: rot_vectors_batch,\n",
    "                 keep_prob: keep_prob_val,\n",
    "                 phase: train\n",
    "                }\n",
    "    t = time.time()-a\n",
    "    #print(\"Construct feed dict: %f\" %(t))\n",
    "    return(feed_dict)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2120,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-0.9129],\n",
       "       [ 3.097 ],\n",
       "       [ 1.0673],\n",
       "       [ 2.94  ],\n",
       "       [-1.0918],\n",
       "       [ 0.9408],\n",
       "       [-0.2029],\n",
       "       [ 2.0485],\n",
       "       [-2.2021],\n",
       "       [ 1.5615],\n",
       "       [ 2.6378],\n",
       "       [-0.9512],\n",
       "       [ 2.1866],\n",
       "       [ 2.2421],\n",
       "       [-0.9033],\n",
       "       [ 0.    ]])"
      ]
     },
     "execution_count": 2120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features[0][3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 2121,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def generate_molecule(mol_file, new_coords, new_file):\n",
    "    print(mol_file)\n",
    "    print(new_coords[:4])\n",
    "    if \".pdb\" in mol_file:\n",
    "        mol = Chem.MolFromPDBFile(mol_file)\n",
    "    else:\n",
    "        mol = Chem.MolFromMol2File(mol_file)\n",
    "    c = mol.GetConformer(0)\n",
    "    for i in range(mol.GetNumAtoms()):\n",
    "        c.SetAtomPosition(i, new_coords[i].tolist())\n",
    "    Chem.MolToMolFile(mol, new_file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2122,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.145103931427\n",
      "Training epoch 0\n",
      "Iter: 0\n",
      "D loss: 8.762e+03\n",
      "G_loss: 3.829e+03\n",
      "4wks:\n",
      "\n",
      "real:\n",
      "[[ 1.514   1.9077 -1.4783]\n",
      " [ 0.174   1.7917 -1.9773]\n",
      " [-0.051   1.5367 -3.3643]\n",
      " [-0.589   3.1577 -1.6003]]\n",
      "fake:\n",
      "[[ 1.514   1.9077 -1.4783]\n",
      " [ 0.174   1.7917 -1.9773]\n",
      " [-0.051   1.5367 -3.3643]\n",
      " [-0.589   3.1577 -1.6003]]\n",
      "fake:\n",
      "[[ 1.  1.  1.]\n",
      " [ 1.  1.  1.]\n",
      " [ 1.  1.  1.]]\n",
      "-0.912921\n",
      "-0.310534\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4a0f53dcd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/home/evan/Documents/deep_docking/datasets/v2015/4a9n/4a9n_ligand.mol2\n",
      "[[ 1.514   1.9077 -1.4783]\n",
      " [ 0.174   1.7917 -1.9773]\n",
      " [-0.051   1.5367 -3.3643]\n",
      " [-0.589   3.1577 -1.6003]]\n",
      "Butane:\n",
      "\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4a698f1450>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0414018630981\n",
      "Training epoch 1000\n",
      "Iter: 1000\n",
      "D loss: 79.31\n",
      "G_loss: 412.7\n",
      "4wks:\n",
      "\n",
      "real:\n",
      "[[ 1.514   1.9077 -1.4783]\n",
      " [ 0.174   1.7917 -1.9773]\n",
      " [-0.051   1.5367 -3.3643]\n",
      " [-0.589   3.1577 -1.6003]]\n",
      "fake:\n",
      "[[ 1.514   1.9077 -1.4783]\n",
      " [ 0.174   1.7917 -1.9773]\n",
      " [-0.051   1.5367 -3.3643]\n",
      " [-0.589   3.1577 -1.6003]]\n",
      "fake:\n",
      "[[ 1.  1.  1.]\n",
      " [ 1.  1.  1.]\n",
      " [ 1.  1.  1.]]\n",
      "-0.912921\n",
      "0.961161\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4a68a6c090>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/home/evan/Documents/deep_docking/datasets/v2015/4a9n/4a9n_ligand.mol2\n",
      "[[ 1.514   1.9077 -1.4783]\n",
      " [ 0.174   1.7917 -1.9773]\n",
      " [-0.051   1.5367 -3.3643]\n",
      " [-0.589   3.1577 -1.6003]]\n",
      "Butane:\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4a0e1a9750>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.039078950882\n",
      "Training epoch 2000\n",
      "Iter: 2000\n",
      "D loss: 56.85\n",
      "G_loss: 416.0\n",
      "4wks:\n",
      "\n",
      "real:\n",
      "[[ 1.514   1.9077 -1.4783]\n",
      " [ 0.174   1.7917 -1.9773]\n",
      " [-0.051   1.5367 -3.3643]\n",
      " [-0.589   3.1577 -1.6003]]\n",
      "fake:\n",
      "[[ 1.514   1.9077 -1.4783]\n",
      " [ 0.174   1.7917 -1.9773]\n",
      " [-0.051   1.5367 -3.3643]\n",
      " [-0.589   3.1577 -1.6003]]\n",
      "fake:\n",
      "[[ 1.  1.  1.]\n",
      " [ 1.  1.  1.]\n",
      " [ 1.  1.  1.]]\n",
      "-0.912921\n",
      "1.7725\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4a6b144610>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/home/evan/Documents/deep_docking/datasets/v2015/4a9n/4a9n_ligand.mol2\n",
      "[[ 1.514   1.9077 -1.4783]\n",
      " [ 0.174   1.7917 -1.9773]\n",
      " [-0.051   1.5367 -3.3643]\n",
      " [-0.589   3.1577 -1.6003]]\n",
      "Butane:\n",
      "\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4a0f09b510>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0386309623718\n",
      "Training epoch 3000\n",
      "Iter: 3000\n",
      "D loss: 39.27\n",
      "G_loss: 501.4\n",
      "4wks:\n",
      "\n",
      "real:\n",
      "[[ 1.514   1.9077 -1.4783]\n",
      " [ 0.174   1.7917 -1.9773]\n",
      " [-0.051   1.5367 -3.3643]\n",
      " [-0.589   3.1577 -1.6003]]\n",
      "fake:\n",
      "[[ 1.514   1.9077 -1.4783]\n",
      " [ 0.174   1.7917 -1.9773]\n",
      " [-0.051   1.5367 -3.3643]\n",
      " [-0.589   3.1577 -1.6003]]\n",
      "fake:\n",
      "[[ 1.  1.  1.]\n",
      " [ 1.  1.  1.]\n",
      " [ 1.  1.  1.]]\n",
      "-0.912921\n",
      "1.67418\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4a1771aa10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/home/evan/Documents/deep_docking/datasets/v2015/4a9n/4a9n_ligand.mol2\n",
      "[[ 1.514   1.9077 -1.4783]\n",
      " [ 0.174   1.7917 -1.9773]\n",
      " [-0.051   1.5367 -3.3643]\n",
      " [-0.589   3.1577 -1.6003]]\n",
      "Butane:\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4a0e03fa10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0394699573517\n",
      "Training epoch 4000\n",
      "Iter: 4000\n",
      "D loss: 40.02\n",
      "G_loss: 428.0\n",
      "4wks:\n",
      "\n",
      "real:\n",
      "[[ 1.514   1.9077 -1.4783]\n",
      " [ 0.174   1.7917 -1.9773]\n",
      " [-0.051   1.5367 -3.3643]\n",
      " [-0.589   3.1577 -1.6003]]\n",
      "fake:\n",
      "[[ 1.514   1.9077 -1.4783]\n",
      " [ 0.174   1.7917 -1.9773]\n",
      " [-0.051   1.5367 -3.3643]\n",
      " [-0.589   3.1577 -1.6003]]\n",
      "fake:\n",
      "[[ 1.  1.  1.]\n",
      " [ 1.  1.  1.]\n",
      " [ 1.  1.  1.]]\n",
      "-0.912921\n",
      "0.99161\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4a15bdcfd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/home/evan/Documents/deep_docking/datasets/v2015/4a9n/4a9n_ligand.mol2\n",
      "[[ 1.514   1.9077 -1.4783]\n",
      " [ 0.174   1.7917 -1.9773]\n",
      " [-0.051   1.5367 -3.3643]\n",
      " [-0.589   3.1577 -1.6003]]\n",
      "Butane:\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAW4AAAD8CAYAAABXe05zAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4a696d9650>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0383331775665\n",
      "Training epoch 5000\n",
      "Iter: 5000\n",
      "D loss: 37.41\n",
      "G_loss: 390.6\n",
      "4wks:\n",
      "\n",
      "real:\n",
      "[[ 1.514   1.9077 -1.4783]\n",
      " [ 0.174   1.7917 -1.9773]\n",
      " [-0.051   1.5367 -3.3643]\n",
      " [-0.589   3.1577 -1.6003]]\n",
      "fake:\n",
      "[[ 1.514   1.9077 -1.4783]\n",
      " [ 0.174   1.7917 -1.9773]\n",
      " [-0.051   1.5367 -3.3643]\n",
      " [-0.589   3.1577 -1.6003]]\n",
      "fake:\n",
      "[[ 1.  1.  1.]\n",
      " [ 1.  1.  1.]\n",
      " [ 1.  1.  1.]]\n",
      "-0.912921\n",
      "1.10145\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4a0e003810>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/home/evan/Documents/deep_docking/datasets/v2015/4a9n/4a9n_ligand.mol2\n",
      "[[ 1.514   1.9077 -1.4783]\n",
      " [ 0.174   1.7917 -1.9773]\n",
      " [-0.051   1.5367 -3.3643]\n",
      " [-0.589   3.1577 -1.6003]]\n",
      "Butane:\n",
      "\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4a1596dbd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0385119915009\n",
      "Training epoch 6000\n",
      "Iter: 6000\n",
      "D loss: 34.3\n",
      "G_loss: 522.1\n",
      "4wks:\n",
      "\n",
      "real:\n",
      "[[ 1.514   1.9077 -1.4783]\n",
      " [ 0.174   1.7917 -1.9773]\n",
      " [-0.051   1.5367 -3.3643]\n",
      " [-0.589   3.1577 -1.6003]]\n",
      "fake:\n",
      "[[ 1.514   1.9077 -1.4783]\n",
      " [ 0.174   1.7917 -1.9773]\n",
      " [-0.051   1.5367 -3.3643]\n",
      " [-0.589   3.1577 -1.6003]]\n",
      "fake:\n",
      "[[ 1.  1.  1.]\n",
      " [ 1.  1.  1.]\n",
      " [ 1.  1.  1.]]\n",
      "-0.912921\n",
      "-1.03182\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4a0ea39f10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/home/evan/Documents/deep_docking/datasets/v2015/4a9n/4a9n_ligand.mol2\n",
      "[[ 1.514   1.9077 -1.4783]\n",
      " [ 0.174   1.7917 -1.9773]\n",
      " [-0.051   1.5367 -3.3643]\n",
      " [-0.589   3.1577 -1.6003]]\n",
      "Butane:\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4a696e9690>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0400648117065\n",
      "Training epoch 7000\n",
      "Iter: 7000\n",
      "D loss: 27.44\n",
      "G_loss: 424.7\n",
      "4wks:\n",
      "\n",
      "real:\n",
      "[[ 1.514   1.9077 -1.4783]\n",
      " [ 0.174   1.7917 -1.9773]\n",
      " [-0.051   1.5367 -3.3643]\n",
      " [-0.589   3.1577 -1.6003]]\n",
      "fake:\n",
      "[[ 1.514   1.9077 -1.4783]\n",
      " [ 0.174   1.7917 -1.9773]\n",
      " [-0.051   1.5367 -3.3643]\n",
      " [-0.589   3.1577 -1.6003]]\n",
      "fake:\n",
      "[[ 1.  1.  1.]\n",
      " [ 1.  1.  1.]\n",
      " [ 1.  1.  1.]]\n",
      "-0.912921\n",
      "-0.689853\n"
     ]
    },
    {
     "data": {
      "image/png": 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4b+Aa5m6yewq4sapOTLSwISX5I+Acc3eEv2fS9Yyiu3t9Q1UdTfJO4Ajw56vwswjw9qo6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\nNJXKQNCnRgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4a0df6c790>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/home/evan/Documents/deep_docking/datasets/v2015/4a9n/4a9n_ligand.mol2\n",
      "[[ 1.514   1.9077 -1.4783]\n",
      " [ 0.174   1.7917 -1.9773]\n",
      " [-0.051   1.5367 -3.3643]\n",
      " [-0.589   3.1577 -1.6003]]\n",
      "Butane:\n",
      "\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4a6a7d6e10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0482308864594\n",
      "Training epoch 8000\n",
      "Iter: 8000\n",
      "D loss: 34.09\n",
      "G_loss: 455.3\n",
      "4wks:\n",
      "\n",
      "real:\n",
      "[[ 1.514   1.9077 -1.4783]\n",
      " [ 0.174   1.7917 -1.9773]\n",
      " [-0.051   1.5367 -3.3643]\n",
      " [-0.589   3.1577 -1.6003]]\n",
      "fake:\n",
      "[[ 1.514   1.9077 -1.4783]\n",
      " [ 0.174   1.7917 -1.9773]\n",
      " [-0.051   1.5367 -3.3643]\n",
      " [-0.589   3.1577 -1.6003]]\n",
      "fake:\n",
      "[[ 1.  1.  1.]\n",
      " [ 1.  1.  1.]\n",
      " [ 1.  1.  1.]]\n",
      "-0.912921\n",
      "0.554189\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4a0eb92390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/home/evan/Documents/deep_docking/datasets/v2015/4a9n/4a9n_ligand.mol2\n",
      "[[ 1.514   1.9077 -1.4783]\n",
      " [ 0.174   1.7917 -1.9773]\n",
      " [-0.051   1.5367 -3.3643]\n",
      " [-0.589   3.1577 -1.6003]]\n",
      "Butane:\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4a696e8f10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0398738384247\n",
      "Training epoch 9000\n",
      "Iter: 9000\n",
      "D loss: 33.11\n",
      "G_loss: 488.6\n",
      "4wks:\n",
      "\n",
      "real:\n",
      "[[ 1.514   1.9077 -1.4783]\n",
      " [ 0.174   1.7917 -1.9773]\n",
      " [-0.051   1.5367 -3.3643]\n",
      " [-0.589   3.1577 -1.6003]]\n",
      "fake:\n",
      "[[ 1.514   1.9077 -1.4783]\n",
      " [ 0.174   1.7917 -1.9773]\n",
      " [-0.051   1.5367 -3.3643]\n",
      " [-0.589   3.1577 -1.6003]]\n",
      "fake:\n",
      "[[ 1.  1.  1.]\n",
      " [ 1.  1.  1.]\n",
      " [ 1.  1.  1.]]\n",
      "-0.912921\n",
      "0.33986\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4a70f2bf10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/home/evan/Documents/deep_docking/datasets/v2015/4a9n/4a9n_ligand.mol2\n",
      "[[ 1.514   1.9077 -1.4783]\n",
      " [ 0.174   1.7917 -1.9773]\n",
      " [-0.051   1.5367 -3.3643]\n",
      " [-0.589   3.1577 -1.6003]]\n",
      "Butane:\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4a6f8c2b10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0455288887024\n",
      "Training epoch 10000\n",
      "Iter: 10000\n",
      "D loss: 28.68\n",
      "G_loss: 481.7\n",
      "4wks:\n",
      "\n",
      "real:\n",
      "[[ 1.514   1.9077 -1.4783]\n",
      " [ 0.174   1.7917 -1.9773]\n",
      " [-0.051   1.5367 -3.3643]\n",
      " [-0.589   3.1577 -1.6003]]\n",
      "fake:\n",
      "[[ 1.514   1.9077 -1.4783]\n",
      " [ 0.174   1.7917 -1.9773]\n",
      " [-0.051   1.5367 -3.3643]\n",
      " [-0.589   3.1577 -1.6003]]\n",
      "fake:\n",
      "[[ 1.  1.  1.]\n",
      " [ 1.  1.  1.]\n",
      " [ 1.  1.  1.]]\n",
      "-0.912921\n",
      "1.08175\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAW4AAAD8CAYAAABXe05zAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4a723ac610>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/home/evan/Documents/deep_docking/datasets/v2015/4a9n/4a9n_ligand.mol2\n",
      "[[ 1.514   1.9077 -1.4783]\n",
      " [ 0.174   1.7917 -1.9773]\n",
      " [-0.051   1.5367 -3.3643]\n",
      " [-0.589   3.1577 -1.6003]]\n",
      "Butane:\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4a69f41e90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0411930084229\n",
      "Training epoch 11000\n",
      "Iter: 11000\n",
      "D loss: 25.09\n",
      "G_loss: 417.1\n",
      "4wks:\n",
      "\n",
      "real:\n",
      "[[ 1.514   1.9077 -1.4783]\n",
      " [ 0.174   1.7917 -1.9773]\n",
      " [-0.051   1.5367 -3.3643]\n",
      " [-0.589   3.1577 -1.6003]]\n",
      "fake:\n",
      "[[ 1.514   1.9077 -1.4783]\n",
      " [ 0.174   1.7917 -1.9773]\n",
      " [-0.051   1.5367 -3.3643]\n",
      " [-0.589   3.1577 -1.6003]]\n",
      "fake:\n",
      "[[ 1.  1.  1.]\n",
      " [ 1.  1.  1.]\n",
      " [ 1.  1.  1.]]\n",
      "-0.912921\n",
      "1.10809\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4a16c0c1d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/home/evan/Documents/deep_docking/datasets/v2015/4a9n/4a9n_ligand.mol2\n",
      "[[ 1.514   1.9077 -1.4783]\n",
      " [ 0.174   1.7917 -1.9773]\n",
      " [-0.051   1.5367 -3.3643]\n",
      " [-0.589   3.1577 -1.6003]]\n",
      "Butane:\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4a7013d950>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0387620925903\n",
      "Training epoch 12000\n",
      "Iter: 12000\n",
      "D loss: 33.16\n",
      "G_loss: 471.8\n",
      "4wks:\n",
      "\n",
      "real:\n",
      "[[ 1.514   1.9077 -1.4783]\n",
      " [ 0.174   1.7917 -1.9773]\n",
      " [-0.051   1.5367 -3.3643]\n",
      " [-0.589   3.1577 -1.6003]]\n",
      "fake:\n",
      "[[ 1.514   1.9077 -1.4783]\n",
      " [ 0.174   1.7917 -1.9773]\n",
      " [-0.051   1.5367 -3.3643]\n",
      " [-0.589   3.1577 -1.6003]]\n",
      "fake:\n",
      "[[ 1.  1.  1.]\n",
      " [ 1.  1.  1.]\n",
      " [ 1.  1.  1.]]\n",
      "-0.912921\n",
      "1.07731\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4a6dd39250>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/home/evan/Documents/deep_docking/datasets/v2015/4a9n/4a9n_ligand.mol2\n",
      "[[ 1.514   1.9077 -1.4783]\n",
      " [ 0.174   1.7917 -1.9773]\n",
      " [-0.051   1.5367 -3.3643]\n",
      " [-0.589   3.1577 -1.6003]]\n",
      "Butane:\n",
      "\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4a69a16750>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.042240858078\n",
      "Training epoch 13000\n",
      "Iter: 13000\n",
      "D loss: 28.04\n",
      "G_loss: 449.2\n",
      "4wks:\n",
      "\n",
      "real:\n",
      "[[ 1.514   1.9077 -1.4783]\n",
      " [ 0.174   1.7917 -1.9773]\n",
      " [-0.051   1.5367 -3.3643]\n",
      " [-0.589   3.1577 -1.6003]]\n",
      "fake:\n",
      "[[ 1.514   1.9077 -1.4783]\n",
      " [ 0.174   1.7917 -1.9773]\n",
      " [-0.051   1.5367 -3.3643]\n",
      " [-0.589   3.1577 -1.6003]]\n",
      "fake:\n",
      "[[ 1.  1.  1.]\n",
      " [ 1.  1.  1.]\n",
      " [ 1.  1.  1.]]\n",
      "-0.912921\n",
      "0.975813\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4a6aaed650>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/home/evan/Documents/deep_docking/datasets/v2015/4a9n/4a9n_ligand.mol2\n",
      "[[ 1.514   1.9077 -1.4783]\n",
      " [ 0.174   1.7917 -1.9773]\n",
      " [-0.051   1.5367 -3.3643]\n",
      " [-0.589   3.1577 -1.6003]]\n",
      "Butane:\n",
      "\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4a6d6a8110>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0449500083923\n",
      "Training epoch 14000\n",
      "Iter: 14000\n",
      "D loss: 25.84\n",
      "G_loss: 453.1\n",
      "4wks:\n",
      "\n",
      "real:\n",
      "[[ 1.514   1.9077 -1.4783]\n",
      " [ 0.174   1.7917 -1.9773]\n",
      " [-0.051   1.5367 -3.3643]\n",
      " [-0.589   3.1577 -1.6003]]\n",
      "fake:\n",
      "[[ 1.514   1.9077 -1.4783]\n",
      " [ 0.174   1.7917 -1.9773]\n",
      " [-0.051   1.5367 -3.3643]\n",
      " [-0.589   3.1577 -1.6003]]\n",
      "fake:\n",
      "[[ 1.  1.  1.]\n",
      " [ 1.  1.  1.]\n",
      " [ 1.  1.  1.]]\n",
      "-0.912921\n",
      "1.39114\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAW4AAAD8CAYAAABXe05zAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAADPFJREFUeJzt3X+s3fVdx/HXa22RH0MxcmIQuF7+WIiEODAn+GMLURiz\nrAtoMhOIW7If5maJODCaWV0imWZJjWaZMcZ4M9CZVZYNaLKs24RlLJNEut123dYfYBDLAHG9uCB0\nJmLh5R/3NGvvzr3nc8r5nnPf5flIbuhpv719AeXJ6fd+z/k6iQAAdbxu1gMAAOMh3ABQDOEGgGII\nNwAUQ7gBoBjCDQDFEG4AKIZwA0AxhBsAitncxSe98MILMz8/38WnBoAz0t69e59L0ms5tpNwz8/P\na2lpqYtPDQBnJNtPth7LqRIAKIZwA0AxhBsAiiHcAFAM4QaAYprCbft3bR+0fcD2PbbP7noYAGC4\nkeG2fbGkD0jqJ7lS0iZJt3Q9DAAwXOupks2SzrG9WdK5kv6ju0kAgPWMDHeSZyT9haTvSHpW0n8n\neaDrYQCA4Ua+ctL2j0u6WdJlkp6X9Bnb70zyyVXHLUhakKS5ubkOpgI4k81v3z32zzmyY1sHSza+\nllMlb5H070mWk/yfpPsl/dLqg5IsJukn6fd6TS+3BwCchpZwf0fSL9g+17YlXS/pcLezAABraTnH\nvUfSvZL2Sfr24OcsdrwLALCGpncHTHKnpDs73gIAaMArJwGgGMINAMUQbgAohnADQDGEGwCKIdwA\nUAzhBoBiCDcAFEO4AaAYwg0AxRBuACiGcANAMYQbAIoh3ABQDOEGgGIINwAUQ7gBoJiR4bZ9ue39\nJ328YPuOaYwDAPywkbcuS/KYpKskyfYmSc9I2tXxLgDAGsY9VXK9pH9L8mQXYwAAo40b7lsk3dPF\nEABAm+Zw2z5L0k2SPrPGjy/YXrK9tLy8PKl9AIBVxnnGfaOkfUm+O+wHkywm6Sfp93q9yawDAPyQ\nccJ9qzhNAgAz1xRu2+dJukHS/d3OAQCMMvJyQElK8n1JP9HxFgBAA145CQDFEG4AKIZwA0AxhBsA\niiHcAFAM4QaAYgg3ABRDuAGgGMINAMUQbgAohnADQDGEGwCKIdwAUAzhBoBiCDcAFEO4AaAYwg0A\nxbTeuuwC2/faftT2Ydu/2PUwAMBwTbcuk/SXkr6Y5B22z5J0boebAADrGBlu2z8m6VpJ75akJC9J\neqnbWQCAtbScKrlM0rKkv7P9DdsfH9z1/RS2F2wv2V5aXl6e+FAAwIqWcG+W9HOS/ibJ1ZK+L2n7\n6oOSLCbpJ+n3er0JzwQAnNAS7qclPZ1kz+DxvVoJOQBgBkaGO8l/SnrK9uWD77pe0qFOVwEA1tR6\nVcnvSNo5uKLkCUnv6W4SAGA9TeFOsl9Sv+MtAIAGvHISAIoh3ABQDOEGgGIINwAUQ7gBoBjCDQDF\nEG4AKIZwA0AxhBsAiiHcAFAM4QaAYgg3ABRDuAGgGMINAMUQbgAohnADQDGEGwCKaboDju0jkl6U\n9LKk40m4Gw4AzEjrPScl6VeSPNfZEgBAE06VAEAxreGOpC/Z3mt7YdgBthdsL9leWl5entxCAMAp\nWsP95iRXSbpR0m/bvnb1AUkWk/ST9Hu93kRHAgB+oCncSZ4Z/PWopF2SrulyFABgbSPDbfs82+ef\n+Lakt0o60PUwAMBwLVeV/KSkXbZPHP+PSb7Y6SoAwJpGhjvJE5LeOIUtAIAGXA4IAMUQbgAohnAD\nQDGEGwCKIdwAUAzhBoBiCDcAFEO4AaAYwg0AxRBuACiGcANAMYQbAIoh3ABQDOEGgGIINwAUQ7gB\noBjCDQDFNIfb9ibb37D9uS4HAQDWN84z7tslHe5qCACgTVO4bV8iaZukj3c7BwAwSstd3iXpY5I+\nKOn8tQ6wvSBpQZLm5uZe/TIAGGF+++6xjj+yY1tHS6Zr5DNu22+XdDTJ3vWOS7KYpJ+k3+v1JjYQ\nAHCqllMlb5J0k+0jkj4l6Trbn+x0FQBgTSPDneQPk1ySZF7SLZK+nOSdnS8DAAzFddwAUEzrFycl\nSUm+IukrnSwBADThGTcAFEO4AaAYwg0AxRBuACiGcANAMYQbAIoh3ABQDOEGgGIINwAUQ7gBoBjC\nDQDFEG4AKIZwA0AxhBsAiiHcAFAM4QaAYlpuFny27a/Z/qbtg7Y/PI1hAIDhWu6A87+SrktyzPYW\nSQ/b/kKSRzreBgAYYmS4k0TSscHDLYOPdDkKALC2pnPctjfZ3i/pqKQHk+zpdhYAYC1NNwtO8rKk\nq2xfIGmX7SuTHDj5GNsLkhYkaW5ubuJDsXHNb9899s85smNbp7/GuJ8fGGaj/r4b66qSJM9LekjS\n1iE/tpikn6Tf6/UmtQ8AsErLVSW9wTNt2T5H0g2SHu16GABguJZTJRdJ+oTtTVoJ/aeTfK7bWQCA\ntbRcVfItSVdPYQsAoAGvnASAYgg3ABRDuAGgGMINAMUQbgAohnADQDGEGwCKIdwAUAzhBoBiCDcA\nFEO4AaAYwg0AxRBuACiGcANAMYQbAIoh3ABQDOEGgGJa7jl5qe2HbB+yfdD27dMYBgAYruWek8cl\n/V6SfbbPl7TX9oNJDnW8DQAwxMhn3EmeTbJv8O0XJR2WdHHXwwAAw411jtv2vFZuHLynizEAgNFa\nTpVIkmy/XtJ9ku5I8sKQH1+QtCBJc3NzExuIV29+++6xjj+yY1tHS9Al/j2/djQ947a9RSvR3pnk\n/mHHJFlM0k/S7/V6k9wIADhJy1UllnSXpMNJPtr9JADAelqecb9J0rskXWd7/+DjbR3vAgCsYeQ5\n7iQPS/IUtgAAGvDKSQAohnADQDGEGwCKIdwAUAzhBoBiCDcAFEO4AaAYwg0AxRBuACiGcANAMYQb\nAIoh3ABQDOEGgGIINwAUQ7gBoBjCDQDFEG4AKKblnpN32z5q+8A0BgEA1tfyjPvvJW3teAcAoNHI\ncCf5qqTvTWELAKAB57gBoJiRd3lvZXtB0oIkzc3Nnfbnmd++e6zjj+zYVvrzn86vAUzC6fxere5M\n+Xue2DPuJItJ+kn6vV5vUp8WALAKp0oAoJiWywHvkfQvki63/bTt93U/CwCwlpHnuJPcOo0hAIA2\nnCoBgGIINwAUQ7gBoBjCDQDFEG4AKIZwA0AxhBsAiiHcAFAM4QaAYgg3ABRDuAGgGMINAMUQbgAo\nhnADQDGEGwCKIdwAUAzhBoBimsJte6vtx2w/bnt716MAAGtruefkJkl/LelGSVdIutX2FV0PAwAM\n1/KM+xpJjyd5IslLkj4l6eZuZwEA1tIS7oslPXXS46cH3wcAmAEnWf8A+x2Stib5rcHjd0n6+SS3\nrTpuQdLC4OHlkh6b/NxTXCjpuY5/jS6we7oq7q64WWL3q/XTSXotB25uOOYZSZee9PiSwfedIsmi\npMWmeRNgeylJf1q/3qSwe7oq7q64WWL3NLWcKvm6pDfYvsz2WZJukfTZbmcBANYy8hl3kuO2b5P0\nT5I2Sbo7ycHOlwEAhmo5VaIkn5f0+Y63jGtqp2UmjN3TVXF3xc0Su6dm5BcnAQAbCy95B4BiSofb\n9p/a/pbt/bYfsP1Ts97Uwvaf2350sH2X7QtmvamF7d+wfdD2K7Y39FfhK75Ng+27bR+1fWDWW8Zh\n+1LbD9k+NPj9cfusN41i+2zbX7P9zcHmD8960zhKnyqx/aNJXhh8+wOSrkjy/hnPGsn2WyV9efCF\n3z+TpCR/MONZI9n+GUmvSPpbSb+fZGnGk4YavE3Dv0q6QSsvGPu6pFuTHJrpsBFsXyvpmKR/SHLl\nrPe0sn2RpIuS7LN9vqS9kn5tI//ztm1J5yU5ZnuLpIcl3Z7kkRlPa1L6GfeJaA+cJ6nE/4WSPJDk\n+ODhI1q5Nn7DS3I4SdcvrJqEkm/TkOSrkr436x3jSvJskn2Db78o6bA2+Kurs+LY4OGWwUeJfkjF\nwy1Jtj9i+ylJvynpj2e95zS8V9IXZj3iDMPbNMyI7XlJV0vaM9slo9neZHu/pKOSHkyy4TefsOHD\nbftLtg8M+bhZkpJ8KMmlknZKum39zzY9o3YPjvmQpONa2b4htOwGhrH9ekn3Sbpj1Z+GN6QkLye5\nSit/4r3GdpnTU03Xcc9Skrc0HrpTK9ea39nhnGajdtt+t6S3S7o+G+gLDWP8897Imt6mAZMzOE98\nn6SdSe6f9Z5xJHne9kOStkoq8YXhDf+Mez2233DSw5slPTqrLeOwvVXSByXdlOR/Zr3nDMTbNEzR\n4At9d0k6nOSjs97TwnbvxNVcts/RyheyS/RDqn9VyX1aeSfCVyQ9Ken9STb8Myvbj0v6EUn/Nfiu\nR4pcDfPrkv5KUk/S85L2J/nV2a4azvbbJH1MP3ibho/MeNJItu+R9Mtaebe670q6M8ldMx3VwPab\nJf2zpG9r5b9FSfqjwSuuNyTbPyvpE1r5/fE6SZ9O8iezXdWudLgB4LWo9KkSAHgtItwAUAzhBoBi\nCDcAFEO4AaAYwg0AxRBuACiGcANAMf8PgDQ2VR0iZsQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4a687aa790>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/home/evan/Documents/deep_docking/datasets/v2015/4a9n/4a9n_ligand.mol2\n",
      "[[ 1.514   1.9077 -1.4783]\n",
      " [ 0.174   1.7917 -1.9773]\n",
      " [-0.051   1.5367 -3.3643]\n",
      " [-0.589   3.1577 -1.6003]]\n",
      "Butane:\n",
      "\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4a6b2c6090>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0395560264587\n",
      "Training epoch 15000\n",
      "Iter: 15000\n",
      "D loss: 38.13\n",
      "G_loss: 460.1\n",
      "4wks:\n",
      "\n",
      "real:\n",
      "[[ 1.514   1.9077 -1.4783]\n",
      " [ 0.174   1.7917 -1.9773]\n",
      " [-0.051   1.5367 -3.3643]\n",
      " [-0.589   3.1577 -1.6003]]\n",
      "fake:\n",
      "[[ 1.514   1.9077 -1.4783]\n",
      " [ 0.174   1.7917 -1.9773]\n",
      " [-0.051   1.5367 -3.3643]\n",
      " [-0.589   3.1577 -1.6003]]\n",
      "fake:\n",
      "[[ 1.  1.  1.]\n",
      " [ 1.  1.  1.]\n",
      " [ 1.  1.  1.]]\n",
      "-0.912921\n",
      "0.890358\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAW4AAAD8CAYAAABXe05zAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nABRDuAGgGMINAMUQbgAohnADQDH/A2FOck9NeHgtAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4a69798350>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/home/evan/Documents/deep_docking/datasets/v2015/4a9n/4a9n_ligand.mol2\n",
      "[[ 1.514   1.9077 -1.4783]\n",
      " [ 0.174   1.7917 -1.9773]\n",
      " [-0.051   1.5367 -3.3643]\n",
      " [-0.589   3.1577 -1.6003]]\n",
      "Butane:\n",
      "\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4a6fc5e1d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0475578308105\n",
      "Training epoch 16000\n",
      "Iter: 16000\n",
      "D loss: 31.79\n",
      "G_loss: 513.1\n",
      "4wks:\n",
      "\n",
      "real:\n",
      "[[ 1.514   1.9077 -1.4783]\n",
      " [ 0.174   1.7917 -1.9773]\n",
      " [-0.051   1.5367 -3.3643]\n",
      " [-0.589   3.1577 -1.6003]]\n",
      "fake:\n",
      "[[ 1.514   1.9077 -1.4783]\n",
      " [ 0.174   1.7917 -1.9773]\n",
      " [-0.051   1.5367 -3.3643]\n",
      " [-0.589   3.1577 -1.6003]]\n",
      "fake:\n",
      "[[ 1.  1.  1.]\n",
      " [ 1.  1.  1.]\n",
      " [ 1.  1.  1.]]\n",
      "-0.912921\n",
      "1.12556\n"
     ]
    },
    {
     "data": {
      "image/png": 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ww+yd3u8ed56+ujvRN1XVsSTvBI4Cf7kGvt8B3l5VM0nWA08At3d32DdhzR25/7LYO28H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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4a17b6f150>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/home/evan/Documents/deep_docking/datasets/v2015/4a9n/4a9n_ligand.mol2\n",
      "[[ 1.514   1.9077 -1.4783]\n",
      " [ 0.174   1.7917 -1.9773]\n",
      " [-0.051   1.5367 -3.3643]\n",
      " [-0.589   3.1577 -1.6003]]\n",
      "Butane:\n",
      "\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4a0e201d50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0396540164948\n",
      "Training epoch 17000\n",
      "Iter: 17000\n",
      "D loss: 38.56\n",
      "G_loss: 512.3\n",
      "4wks:\n",
      "\n",
      "real:\n",
      "[[ 1.514   1.9077 -1.4783]\n",
      " [ 0.174   1.7917 -1.9773]\n",
      " [-0.051   1.5367 -3.3643]\n",
      " [-0.589   3.1577 -1.6003]]\n",
      "fake:\n",
      "[[ 1.514   1.9077 -1.4783]\n",
      " [ 0.174   1.7917 -1.9773]\n",
      " [-0.051   1.5367 -3.3643]\n",
      " [-0.589   3.1577 -1.6003]]\n",
      "fake:\n",
      "[[ 1.  1.  1.]\n",
      " [ 1.  1.  1.]\n",
      " [ 1.  1.  1.]]\n",
      "-0.912921\n",
      "1.54136\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAW4AAAD8CAYAAABXe05zAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4a680808d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/home/evan/Documents/deep_docking/datasets/v2015/4a9n/4a9n_ligand.mol2\n",
      "[[ 1.514   1.9077 -1.4783]\n",
      " [ 0.174   1.7917 -1.9773]\n",
      " [-0.051   1.5367 -3.3643]\n",
      " [-0.589   3.1577 -1.6003]]\n",
      "Butane:\n",
      "\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4a3c10fd90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.049309015274\n",
      "Training epoch 18000\n",
      "Iter: 18000\n",
      "D loss: 39.76\n",
      "G_loss: 444.8\n",
      "4wks:\n",
      "\n",
      "real:\n",
      "[[ 1.514   1.9077 -1.4783]\n",
      " [ 0.174   1.7917 -1.9773]\n",
      " [-0.051   1.5367 -3.3643]\n",
      " [-0.589   3.1577 -1.6003]]\n",
      "fake:\n",
      "[[ 1.514   1.9077 -1.4783]\n",
      " [ 0.174   1.7917 -1.9773]\n",
      " [-0.051   1.5367 -3.3643]\n",
      " [-0.589   3.1577 -1.6003]]\n",
      "fake:\n",
      "[[ 1.  1.  1.]\n",
      " [ 1.  1.  1.]\n",
      " [ 1.  1.  1.]]\n",
      "-0.912921\n",
      "0.6201\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAW4AAAD8CAYAAABXe05zAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nb/Xy0P8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4a68765fd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/home/evan/Documents/deep_docking/datasets/v2015/4a9n/4a9n_ligand.mol2\n",
      "[[ 1.514   1.9077 -1.4783]\n",
      " [ 0.174   1.7917 -1.9773]\n",
      " [-0.051   1.5367 -3.3643]\n",
      " [-0.589   3.1577 -1.6003]]\n",
      "Butane:\n",
      "\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4a15bf02d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0393059253693\n",
      "Training epoch 19000\n",
      "Iter: 19000\n",
      "D loss: 36.37\n",
      "G_loss: 409.8\n",
      "4wks:\n",
      "\n",
      "real:\n",
      "[[ 1.514   1.9077 -1.4783]\n",
      " [ 0.174   1.7917 -1.9773]\n",
      " [-0.051   1.5367 -3.3643]\n",
      " [-0.589   3.1577 -1.6003]]\n",
      "fake:\n",
      "[[ 1.514   1.9077 -1.4783]\n",
      " [ 0.174   1.7917 -1.9773]\n",
      " [-0.051   1.5367 -3.3643]\n",
      " [-0.589   3.1577 -1.6003]]\n",
      "fake:\n",
      "[[ 1.  1.  1.]\n",
      " [ 1.  1.  1.]\n",
      " [ 1.  1.  1.]]\n",
      "-0.912921\n",
      "0.319298\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAW4AAAD8CAYAAABXe05zAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAC/RJREFUeJzt3X+o3XUdx/HXq22imSXkqcR5u/4RA5HSuJhiRM2M6cQo\nDBQS7Af3nwwFQSb+5X9CIAZFcVEryIxIR+HUWriQIK1Np0w3o2TRxNpExB+BMn31xz3LOc+953Ou\n53vP3rvPB1y8557POff9dXdPvvue7/dcJxEAoI73TXoAAMBoCDcAFEO4AaAYwg0AxRBuACiGcANA\nMYQbAIoh3ABQDOEGgGJWd/Gkp5xySqanp7t4agA4Ju3YseOFJL2WtZ2Ee3p6Wtu3b+/iqQHgmGT7\nn61rOVQCAMUQbgAohnADQDGEGwCKIdwAUMzQcNteZ3vnYR8v275uOYYDALzb0NMBkzwj6WxJsr1K\n0nOSNnc8FwBgAaMeKrlQ0j+SNJ9vCAAYr1HDfYWku7sYBADQpvnKSdvHSbpM0o0L3D8raVaSpqam\nxjJcRdObtnT+PfbesrHz7wHg6DXKHvfFkh5L8p9BdyaZSzKTZKbXa7rcHgCwBKOE+0pxmAQAJq4p\n3LZPlHSRpHu7HQcAMEzTMe4kr0n6cMezAAAacOUkABRDuAGgGMINAMUQbgAohnADQDGEGwCKIdwA\nUAzhBoBiCDcAFEO4AaAYwg0AxRBuACiGcANAMYQbAIoh3ABQDOEGgGIINwAUQ7gBoBjCDQDFEG4A\nKIZwA0AxTeG2fbLtX9veY3u37fO7HgwAMNjqxnXfl/RgksttHyfp/R3OBABYxNBw2/6QpM9JulqS\nkrwh6Y1uxwIALKTlUMkZkg5I+ontx23fbvvEIxfZnrW93fb2AwcOjH1QAMC8lnCvlvRpST9Kco6k\n1yRtOnJRkrkkM0lmer3emMcEABzSEu59kvYlebR/+9eaDzkAYAKGhjvJvyX9y/a6/pculPR0p1MB\nABbUelbJdyXd1T+j5FlJ3+huJADAYprCnWSnpJmOZwEANODKSQAohnADQDGEGwCKIdwAUAzhBoBi\nCDcAFEO4AaAYwg0AxRBuACiGcANAMYQbAIoh3ABQDOEGgGIINwAUQ7gBoBjCDQDFEG4AKIZwA0Ax\nhBsAiiHcAFBM0y8Ltr1X0iuS3pR0MAm/OBgAJqQp3H1fSPJCZ5MAAJpwqAQAimkNdyT9wfYO27Nd\nDgQAWFzroZLPJnnO9kckbbW9J8nDhy/oB31WkqampsY8JgDgkKY97iTP9f+7X9JmSecOWDOXZCbJ\nTK/XG++UAID/Gxpu2yfaPunQ55K+JGlX14MBAAZrOVTyUUmbbR9a/4skD3Y6FQBgQUPDneRZSZ9a\nhlkAAA04HRAAiiHcAFAM4QaAYgg3ABRDuAGgGMINAMUQbgAohnADQDGEGwCKIdwAUAzhBoBiCDcA\nFEO4AaAYwg0AxRBuACiGcANAMYQbAIoh3ABQDOEGgGIINwAUQ7gBoJjmcNteZftx2/d1ORAAYHGj\n7HFfK2l3V4MAANo0hdv2WkkbJd3e7TgAgGFa97hvk3SDpLcWWmB71vZ229sPHDgwluEAAO82NNy2\nL5W0P8mOxdYlmUsyk2Sm1+uNbUAAwDu17HFfIOky23sl/VLSets/73QqAMCChoY7yY1J1iaZlnSF\npIeSfL3zyQAAA3EeNwAUs3qUxUn+KOmPnUwCAGjCHjcAFEO4AaAYwg0AxRBuACiGcANAMYQbAIoh\n3ABQDOEGgGIINwAUQ7gBoBjCDQDFEG4AKIZwA0AxhBsAiiHcAFAM4QaAYgg3ABRDuAGgGMINAMUQ\nbgAoZmi4bR9v+y+2n7D9lO2bl2MwAMBgLb/l/XVJ65O8anuNpD/ZfiDJIx3PBgAYYGi4k0TSq/2b\na/of6XIoAMDCmo5x215le6ek/ZK2Jnm027EAAAtpOVSiJG9KOtv2yZI22z4rya7D19ielTQrSVNT\nU2MfFG+b3rRlpPV7b9nY0STAwkb9OV2KlfqzPdJZJUlekrRN0oYB980lmUky0+v1xjUfAOAILWeV\n9Pp72rJ9gqSLJO3pejAAwGAth0pOlfQz26s0H/pfJbmv27EAAAtpOavkSUnnLMMsAIAGXDkJAMUQ\nbgAohnADQDGEGwCKIdwAUAzhBoBiCDcAFEO4AaAYwg0AxRBuACiGcANAMYQbAIoh3ABQDOEGgGII\nNwAUQ7gBoBjCDQDFEG4AKIZwA0AxhBsAiiHcAFDM0HDbPt32NttP237K9rXLMRgAYLDVDWsOSro+\nyWO2T5K0w/bWJE93PBsAYIChe9xJnk/yWP/zVyTtlnRa14MBAAYb6Ri37WlJ50h6tIthAADDtRwq\nkSTZ/oCkeyRdl+TlAffPSpqVpKmpqSUPNL1py0jr996yccnfCwAWc7T2qGmP2/YazUf7riT3DlqT\nZC7JTJKZXq83zhkBAIdpOavEku6QtDvJrd2PBABYTMse9wWSrpK03vbO/sclHc8FAFjA0GPcSf4k\nycswCwCgAVdOAkAxhBsAiiHcAFAM4QaAYgg3ABRDuAGgGMINAMUQbgAohnADQDGEGwCKIdwAUAzh\nBoBiCDcAFEO4AaAYwg0AxRBuACiGcANAMYQbAIoh3ABQDOEGgGIINwAUMzTctu+0vd/2ruUYCACw\nuJY97p9K2tDxHACARkPDneRhSS8uwywAgAarx/VEtmclzUrS1NTUuJ524qY3bZn0CEe9pfw/2nvL\nxg4mObZ0/bO3Ev8MjpW/z2N7cTLJXJKZJDO9Xm9cTwsAOAJnlQBAMYQbAIppOR3wbkl/lrTO9j7b\n3+p+LADAQoa+OJnkyuUYBADQhkMlAFAM4QaAYgg3ABRDuAGgGMINAMUQbgAohnADQDGEGwCKIdwA\nUAzhBoBiCDcAFEO4AaAYwg0AxRBuACiGcANAMYQbAIoh3ABQDOEGgGIINwAUQ7gBoJimcNveYPsZ\n23+3vanroQAACxsabturJP1Q0sWSzpR0pe0zux4MADBYyx73uZL+nuTZJG9I+qWkL3c7FgBgIS3h\nPk3Svw67va//NQDABDjJ4gvsyyVtSPLt/u2rJH0myTVHrJuVNNu/uU7SM+Mfd6xOkfTCpId4D6rP\nL9XfhurzS/W3ofr80tvb8PEkvZYHrG5Y85yk0w+7vbb/tXdIMidpruWbHg1sb08yM+k5lqr6/FL9\nbag+v1R/G6rPLy1tG1oOlfxV0idsn2H7OElXSPrtUgYEALx3Q/e4kxy0fY2k30laJenOJE91PhkA\nYKCWQyVKcr+k+zueZbmVOayzgOrzS/W3ofr8Uv1tqD6/tIRtGPriJADg6MIl7wBQzIoNt+3v2d5j\n+0nbm22fPOmZRmX7a7afsv2W7TKvrFd/CwXbd9reb3vXpGdZCtun295m++n+z8+1k55pVLaPt/0X\n20/0t+HmSc+0FLZX2X7c9n2jPG7FhlvSVklnJfmkpL9JunHC8yzFLklflfTwpAdpdYy8hcJPJW2Y\n9BDvwUFJ1yc5U9J5kr5T8M/gdUnrk3xK0tmSNtg+b8IzLcW1knaP+qAVG+4kv09ysH/zEc2fn15K\nkt1JjvYLnY5U/i0Ukjws6cVJz7FUSZ5P8lj/81c0H45SV0Nn3qv9m2v6H6VesLO9VtJGSbeP+tgV\nG+4jfFPSA5MeYoXgLRSOIranJZ0j6dHJTjK6/mGGnZL2S9qapNo23CbpBklvjfrAptMBq7L9B0kf\nG3DXTUl+019zk+b/6XjXcs7WqmUbgKWw/QFJ90i6LsnLk55nVEnelHR2//WpzbbPSlLidQfbl0ra\nn2SH7c+P+vhjOtxJvrjY/bavlnSppAtzlJ4XOWwbCmp6CwV0y/YazUf7riT3Tnqe9yLJS7a3af51\nhxLhlnSBpMtsXyLpeEkftP3zJF9vefCKPVRie4Pm/5lyWZL/TnqeFYS3UJgw25Z0h6TdSW6d9DxL\nYbt36Eww2ydIukjSnslO1S7JjUnWJpnW/N+Bh1qjLa3gcEv6gaSTJG21vdP2jyc90Khsf8X2Pknn\nS9pi+3eTnmmY/gvCh95CYbekX1V7CwXbd0v6s6R1tvfZ/takZxrRBZKukrS+/7O/s7/nV8mpkrbZ\nflLzOwNbk4x0Sl1lXDkJAMWs5D1uACiJcANAMYQbAIoh3ABQDOEGgGIINwAUQ7gBoBjCDQDF/A9A\n8cGRtT9FLwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4a0e42e290>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/home/evan/Documents/deep_docking/datasets/v2015/4a9n/4a9n_ligand.mol2\n",
      "[[ 1.514   1.9077 -1.4783]\n",
      " [ 0.174   1.7917 -1.9773]\n",
      " [-0.051   1.5367 -3.3643]\n",
      " [-0.589   3.1577 -1.6003]]\n",
      "Butane:\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4a0ea22bd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0638530254364\n",
      "Training epoch 20000\n",
      "Iter: 20000\n",
      "D loss: 41.49\n",
      "G_loss: 433.5\n",
      "4wks:\n",
      "\n",
      "real:\n",
      "[[ 1.514   1.9077 -1.4783]\n",
      " [ 0.174   1.7917 -1.9773]\n",
      " [-0.051   1.5367 -3.3643]\n",
      " [-0.589   3.1577 -1.6003]]\n",
      "fake:\n",
      "[[ 1.514   1.9077 -1.4783]\n",
      " [ 0.174   1.7917 -1.9773]\n",
      " [-0.051   1.5367 -3.3643]\n",
      " [-0.589   3.1577 -1.6003]]\n",
      "fake:\n",
      "[[ 1.  1.  1.]\n",
      " [ 1.  1.  1.]\n",
      " [ 1.  1.  1.]]\n",
      "-0.912921\n",
      "0.685496\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAW4AAAD8CAYAAABXe05zAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4a0fb4c790>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/home/evan/Documents/deep_docking/datasets/v2015/4a9n/4a9n_ligand.mol2\n",
      "[[ 1.514   1.9077 -1.4783]\n",
      " [ 0.174   1.7917 -1.9773]\n",
      " [-0.051   1.5367 -3.3643]\n",
      " [-0.589   3.1577 -1.6003]]\n",
      "Butane:\n",
      "\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4a0fb4c750>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0473470687866\n",
      "Training epoch 21000\n",
      "Iter: 21000\n",
      "D loss: 37.63\n",
      "G_loss: 494.9\n",
      "4wks:\n",
      "\n",
      "real:\n",
      "[[ 1.514   1.9077 -1.4783]\n",
      " [ 0.174   1.7917 -1.9773]\n",
      " [-0.051   1.5367 -3.3643]\n",
      " [-0.589   3.1577 -1.6003]]\n",
      "fake:\n",
      "[[ 1.514   1.9077 -1.4783]\n",
      " [ 0.174   1.7917 -1.9773]\n",
      " [-0.051   1.5367 -3.3643]\n",
      " [-0.589   3.1577 -1.6003]]\n",
      "fake:\n",
      "[[ 1.  1.  1.]\n",
      " [ 1.  1.  1.]\n",
      " [ 1.  1.  1.]]\n",
      "-0.912921\n",
      "-0.0701949\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXQAAAD8CAYAAABn919SAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\noIj/Ad0N7fClu2LIAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4a0fb69a50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/home/evan/Documents/deep_docking/datasets/v2015/4a9n/4a9n_ligand.mol2\n",
      "[[ 1.514   1.9077 -1.4783]\n",
      " [ 0.174   1.7917 -1.9773]\n",
      " [-0.051   1.5367 -3.3643]\n",
      " [-0.589   3.1577 -1.6003]]\n",
      "Butane:\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4a688f0290>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0395810604095\n",
      "Training epoch 22000\n",
      "Iter: 22000\n",
      "D loss: 25.46\n",
      "G_loss: 455.8\n",
      "4wks:\n",
      "\n",
      "real:\n",
      "[[ 1.514   1.9077 -1.4783]\n",
      " [ 0.174   1.7917 -1.9773]\n",
      " [-0.051   1.5367 -3.3643]\n",
      " [-0.589   3.1577 -1.6003]]\n",
      "fake:\n",
      "[[ 1.514   1.9077 -1.4783]\n",
      " [ 0.174   1.7917 -1.9773]\n",
      " [-0.051   1.5367 -3.3643]\n",
      " [-0.589   3.1577 -1.6003]]\n",
      "fake:\n",
      "[[ 1.  1.  1.]\n",
      " [ 1.  1.  1.]\n",
      " [ 1.  1.  1.]]\n",
      "-0.912921\n",
      "-0.0896592\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4a676a4fd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/home/evan/Documents/deep_docking/datasets/v2015/4a9n/4a9n_ligand.mol2\n",
      "[[ 1.514   1.9077 -1.4783]\n",
      " [ 0.174   1.7917 -1.9773]\n",
      " [-0.051   1.5367 -3.3643]\n",
      " [-0.589   3.1577 -1.6003]]\n",
      "Butane:\n",
      "\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4a14055210>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0410490036011\n",
      "Training epoch 23000\n",
      "Iter: 23000\n",
      "D loss: 45.41\n",
      "G_loss: 418.5\n",
      "4wks:\n",
      "\n",
      "real:\n",
      "[[ 1.514   1.9077 -1.4783]\n",
      " [ 0.174   1.7917 -1.9773]\n",
      " [-0.051   1.5367 -3.3643]\n",
      " [-0.589   3.1577 -1.6003]]\n",
      "fake:\n",
      "[[ 1.514   1.9077 -1.4783]\n",
      " [ 0.174   1.7917 -1.9773]\n",
      " [-0.051   1.5367 -3.3643]\n",
      " [-0.589   3.1577 -1.6003]]\n",
      "fake:\n",
      "[[ 1.  1.  1.]\n",
      " [ 1.  1.  1.]\n",
      " [ 1.  1.  1.]]\n",
      "-0.912921\n",
      "0.947869\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAW4AAAD8CAYAAABXe05zAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nhBtgGOEGGEa4AYYRboBh/gfofT3VNiTODQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4a1783e810>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/home/evan/Documents/deep_docking/datasets/v2015/4a9n/4a9n_ligand.mol2\n",
      "[[ 1.514   1.9077 -1.4783]\n",
      " [ 0.174   1.7917 -1.9773]\n",
      " [-0.051   1.5367 -3.3643]\n",
      " [-0.589   3.1577 -1.6003]]\n",
      "Butane:\n",
      "\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4a0f1fc290>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0386230945587\n",
      "Training epoch 24000\n",
      "Iter: 24000\n",
      "D loss: 56.22\n",
      "G_loss: 444.4\n",
      "4wks:\n",
      "\n",
      "real:\n",
      "[[ 1.514   1.9077 -1.4783]\n",
      " [ 0.174   1.7917 -1.9773]\n",
      " [-0.051   1.5367 -3.3643]\n",
      " [-0.589   3.1577 -1.6003]]\n",
      "fake:\n",
      "[[ 1.514   1.9077 -1.4783]\n",
      " [ 0.174   1.7917 -1.9773]\n",
      " [-0.051   1.5367 -3.3643]\n",
      " [-0.589   3.1577 -1.6003]]\n",
      "fake:\n",
      "[[ 1.  1.  1.]\n",
      " [ 1.  1.  1.]\n",
      " [ 1.  1.  1.]]\n",
      "-0.912921\n",
      "-0.980091\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXQAAAD8CAYAAABn919SAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4a67e0b990>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/home/evan/Documents/deep_docking/datasets/v2015/4a9n/4a9n_ligand.mol2\n",
      "[[ 1.514   1.9077 -1.4783]\n",
      " [ 0.174   1.7917 -1.9773]\n",
      " [-0.051   1.5367 -3.3643]\n",
      " [-0.589   3.1577 -1.6003]]\n",
      "Butane:\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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HOIezwMer6v3AlcAdE/Y1/A1wbVX9BXAZsL27e27S3AkcG3eIIf6mqi5b7Vshz6uqem3e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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4a679e6c50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0438480377197\n",
      "Training epoch 25000\n",
      "Iter: 25000\n",
      "D loss: 49.21\n",
      "G_loss: 508.5\n",
      "4wks:\n",
      "\n",
      "real:\n",
      "[[ 1.514   1.9077 -1.4783]\n",
      " [ 0.174   1.7917 -1.9773]\n",
      " [-0.051   1.5367 -3.3643]\n",
      " [-0.589   3.1577 -1.6003]]\n",
      "fake:\n",
      "[[ 1.514   1.9077 -1.4783]\n",
      " [ 0.174   1.7917 -1.9773]\n",
      " [-0.051   1.5367 -3.3643]\n",
      " [-0.589   3.1577 -1.6003]]\n",
      "fake:\n",
      "[[ 1.  1.  1.]\n",
      " [ 1.  1.  1.]\n",
      " [ 1.  1.  1.]]\n",
      "-0.912921\n",
      "-0.851486\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4a0eed30d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/home/evan/Documents/deep_docking/datasets/v2015/4a9n/4a9n_ligand.mol2\n",
      "[[ 1.514   1.9077 -1.4783]\n",
      " [ 0.174   1.7917 -1.9773]\n",
      " [-0.051   1.5367 -3.3643]\n",
      " [-0.589   3.1577 -1.6003]]\n",
      "Butane:\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAW4AAAD8CAYAAABXe05zAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4a67dd1210>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0554959774017\n",
      "Training epoch 26000\n",
      "Iter: 26000\n",
      "D loss: 54.55\n",
      "G_loss: 470.3\n",
      "4wks:\n",
      "\n",
      "real:\n",
      "[[ 1.514   1.9077 -1.4783]\n",
      " [ 0.174   1.7917 -1.9773]\n",
      " [-0.051   1.5367 -3.3643]\n",
      " [-0.589   3.1577 -1.6003]]\n",
      "fake:\n",
      "[[ 1.514   1.9077 -1.4783]\n",
      " [ 0.174   1.7917 -1.9773]\n",
      " [-0.051   1.5367 -3.3643]\n",
      " [-0.589   3.1577 -1.6003]]\n",
      "fake:\n",
      "[[ 1.  1.  1.]\n",
      " [ 1.  1.  1.]\n",
      " [ 1.  1.  1.]]\n",
      "-0.912921\n",
      "0.50325\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4a0e03ea90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/home/evan/Documents/deep_docking/datasets/v2015/4a9n/4a9n_ligand.mol2\n",
      "[[ 1.514   1.9077 -1.4783]\n",
      " [ 0.174   1.7917 -1.9773]\n",
      " [-0.051   1.5367 -3.3643]\n",
      " [-0.589   3.1577 -1.6003]]\n",
      "Butane:\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXcAAAD8CAYAAACMwORRAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nIeMuSQ0Zd0lqyLhLUkP/D3peeWEMcqu9AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4a0d7416d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0588347911835\n",
      "Training epoch 27000\n",
      "Iter: 27000\n",
      "D loss: 38.24\n",
      "G_loss: 509.1\n",
      "4wks:\n",
      "\n",
      "real:\n",
      "[[ 1.514   1.9077 -1.4783]\n",
      " [ 0.174   1.7917 -1.9773]\n",
      " [-0.051   1.5367 -3.3643]\n",
      " [-0.589   3.1577 -1.6003]]\n",
      "fake:\n",
      "[[ 1.514   1.9077 -1.4783]\n",
      " [ 0.174   1.7917 -1.9773]\n",
      " [-0.051   1.5367 -3.3643]\n",
      " [-0.589   3.1577 -1.6003]]\n",
      "fake:\n",
      "[[ 1.  1.  1.]\n",
      " [ 1.  1.  1.]\n",
      " [ 1.  1.  1.]]\n",
      "-0.912921\n",
      "0.386497\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4a0e2fdfd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/home/evan/Documents/deep_docking/datasets/v2015/4a9n/4a9n_ligand.mol2\n",
      "[[ 1.514   1.9077 -1.4783]\n",
      " [ 0.174   1.7917 -1.9773]\n",
      " [-0.051   1.5367 -3.3643]\n",
      " [-0.589   3.1577 -1.6003]]\n",
      "Butane:\n",
      "\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4a70d7c4d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0420100688934\n",
      "Training epoch 28000\n",
      "Iter: 28000\n",
      "D loss: 35.24\n",
      "G_loss: 396.7\n",
      "4wks:\n",
      "\n",
      "real:\n",
      "[[ 1.514   1.9077 -1.4783]\n",
      " [ 0.174   1.7917 -1.9773]\n",
      " [-0.051   1.5367 -3.3643]\n",
      " [-0.589   3.1577 -1.6003]]\n",
      "fake:\n",
      "[[ 1.514   1.9077 -1.4783]\n",
      " [ 0.174   1.7917 -1.9773]\n",
      " [-0.051   1.5367 -3.3643]\n",
      " [-0.589   3.1577 -1.6003]]\n",
      "fake:\n",
      "[[ 1.  1.  1.]\n",
      " [ 1.  1.  1.]\n",
      " [ 1.  1.  1.]]\n",
      "-0.912921\n",
      "2.89364\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4a6dd3b610>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/home/evan/Documents/deep_docking/datasets/v2015/4a9n/4a9n_ligand.mol2\n",
      "[[ 1.514   1.9077 -1.4783]\n",
      " [ 0.174   1.7917 -1.9773]\n",
      " [-0.051   1.5367 -3.3643]\n",
      " [-0.589   3.1577 -1.6003]]\n",
      "Butane:\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAW4AAAD8CAYAAABXe05zAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\njwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4a696c7090>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0407059192657\n",
      "Training epoch 29000\n",
      "Iter: 29000\n",
      "D loss: 39.92\n",
      "G_loss: 424.8\n",
      "4wks:\n",
      "\n",
      "real:\n",
      "[[ 1.514   1.9077 -1.4783]\n",
      " [ 0.174   1.7917 -1.9773]\n",
      " [-0.051   1.5367 -3.3643]\n",
      " [-0.589   3.1577 -1.6003]]\n",
      "fake:\n",
      "[[ 1.514   1.9077 -1.4783]\n",
      " [ 0.174   1.7917 -1.9773]\n",
      " [-0.051   1.5367 -3.3643]\n",
      " [-0.589   3.1577 -1.6003]]\n",
      "fake:\n",
      "[[ 1.  1.  1.]\n",
      " [ 1.  1.  1.]\n",
      " [ 1.  1.  1.]]\n",
      "-0.912921\n",
      "3.19341\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4a0e2fe210>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/home/evan/Documents/deep_docking/datasets/v2015/4a9n/4a9n_ligand.mol2\n",
      "[[ 1.514   1.9077 -1.4783]\n",
      " [ 0.174   1.7917 -1.9773]\n",
      " [-0.051   1.5367 -3.3643]\n",
      " [-0.589   3.1577 -1.6003]]\n",
      "Butane:\n",
      "\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4a15f81e50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.039381980896\n",
      "Training epoch 30000\n",
      "Iter: 30000\n",
      "D loss: 42.6\n",
      "G_loss: 451.2\n",
      "4wks:\n",
      "\n",
      "real:\n",
      "[[ 1.514   1.9077 -1.4783]\n",
      " [ 0.174   1.7917 -1.9773]\n",
      " [-0.051   1.5367 -3.3643]\n",
      " [-0.589   3.1577 -1.6003]]\n",
      "fake:\n",
      "[[ 1.514   1.9077 -1.4783]\n",
      " [ 0.174   1.7917 -1.9773]\n",
      " [-0.051   1.5367 -3.3643]\n",
      " [-0.589   3.1577 -1.6003]]\n",
      "fake:\n",
      "[[ 1.  1.  1.]\n",
      " [ 1.  1.  1.]\n",
      " [ 1.  1.  1.]]\n",
      "-0.912921\n",
      "3.3572\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4a68934290>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/home/evan/Documents/deep_docking/datasets/v2015/4a9n/4a9n_ligand.mol2\n",
      "[[ 1.514   1.9077 -1.4783]\n",
      " [ 0.174   1.7917 -1.9773]\n",
      " [-0.051   1.5367 -3.3643]\n",
      " [-0.589   3.1577 -1.6003]]\n",
      "Butane:\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAW4AAAD8CAYAAABXe05zAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nG2CY/wOXRWmb6i4e2AAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4a70651450>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0385148525238\n",
      "Training epoch 31000\n",
      "Iter: 31000\n",
      "D loss: 34.07\n",
      "G_loss: 423.2\n",
      "4wks:\n",
      "\n",
      "real:\n",
      "[[ 1.514   1.9077 -1.4783]\n",
      " [ 0.174   1.7917 -1.9773]\n",
      " [-0.051   1.5367 -3.3643]\n",
      " [-0.589   3.1577 -1.6003]]\n",
      "fake:\n",
      "[[ 1.514   1.9077 -1.4783]\n",
      " [ 0.174   1.7917 -1.9773]\n",
      " [-0.051   1.5367 -3.3643]\n",
      " [-0.589   3.1577 -1.6003]]\n",
      "fake:\n",
      "[[ 1.  1.  1.]\n",
      " [ 1.  1.  1.]\n",
      " [ 1.  1.  1.]]\n",
      "-0.912921\n",
      "3.14981\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4a6a6dca10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/home/evan/Documents/deep_docking/datasets/v2015/4a9n/4a9n_ligand.mol2\n",
      "[[ 1.514   1.9077 -1.4783]\n",
      " [ 0.174   1.7917 -1.9773]\n",
      " [-0.051   1.5367 -3.3643]\n",
      " [-0.589   3.1577 -1.6003]]\n",
      "Butane:\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAW4AAAD8CAYAAABXe05zAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nRK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4a0ecf4b50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0389831066132\n",
      "Training epoch 32000\n",
      "Iter: 32000\n",
      "D loss: 31.69\n",
      "G_loss: 485.9\n",
      "4wks:\n",
      "\n",
      "real:\n",
      "[[ 1.514   1.9077 -1.4783]\n",
      " [ 0.174   1.7917 -1.9773]\n",
      " [-0.051   1.5367 -3.3643]\n",
      " [-0.589   3.1577 -1.6003]]\n",
      "fake:\n",
      "[[ 1.514   1.9077 -1.4783]\n",
      " [ 0.174   1.7917 -1.9773]\n",
      " [-0.051   1.5367 -3.3643]\n",
      " [-0.589   3.1577 -1.6003]]\n",
      "fake:\n",
      "[[ 1.  1.  1.]\n",
      " [ 1.  1.  1.]\n",
      " [ 1.  1.  1.]]\n",
      "-0.912921\n",
      "3.08571\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4a0e52c0d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/home/evan/Documents/deep_docking/datasets/v2015/4a9n/4a9n_ligand.mol2\n",
      "[[ 1.514   1.9077 -1.4783]\n",
      " [ 0.174   1.7917 -1.9773]\n",
      " [-0.051   1.5367 -3.3643]\n",
      " [-0.589   3.1577 -1.6003]]\n",
      "Butane:\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAW4AAAD8CAYAAABXe05zAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4a0ed55fd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0479900836945\n",
      "Training epoch 33000\n",
      "Iter: 33000\n",
      "D loss: 41.4\n",
      "G_loss: 410.5\n",
      "4wks:\n",
      "\n",
      "real:\n",
      "[[ 1.514   1.9077 -1.4783]\n",
      " [ 0.174   1.7917 -1.9773]\n",
      " [-0.051   1.5367 -3.3643]\n",
      " [-0.589   3.1577 -1.6003]]\n",
      "fake:\n",
      "[[ 1.514   1.9077 -1.4783]\n",
      " [ 0.174   1.7917 -1.9773]\n",
      " [-0.051   1.5367 -3.3643]\n",
      " [-0.589   3.1577 -1.6003]]\n",
      "fake:\n",
      "[[ 1.  1.  1.]\n",
      " [ 1.  1.  1.]\n",
      " [ 1.  1.  1.]]\n",
      "-0.912921\n",
      "3.22078\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4a0ec560d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/home/evan/Documents/deep_docking/datasets/v2015/4a9n/4a9n_ligand.mol2\n",
      "[[ 1.514   1.9077 -1.4783]\n",
      " [ 0.174   1.7917 -1.9773]\n",
      " [-0.051   1.5367 -3.3643]\n",
      " [-0.589   3.1577 -1.6003]]\n",
      "Butane:\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4a6c5bfed0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0407609939575\n",
      "Training epoch 34000\n",
      "Iter: 34000\n",
      "D loss: 29.64\n",
      "G_loss: 493.0\n",
      "4wks:\n",
      "\n",
      "real:\n",
      "[[ 1.514   1.9077 -1.4783]\n",
      " [ 0.174   1.7917 -1.9773]\n",
      " [-0.051   1.5367 -3.3643]\n",
      " [-0.589   3.1577 -1.6003]]\n",
      "fake:\n",
      "[[ 1.514   1.9077 -1.4783]\n",
      " [ 0.174   1.7917 -1.9773]\n",
      " [-0.051   1.5367 -3.3643]\n",
      " [-0.589   3.1577 -1.6003]]\n",
      "fake:\n",
      "[[ 1.  1.  1.]\n",
      " [ 1.  1.  1.]\n",
      " [ 1.  1.  1.]]\n",
      "-0.912921\n",
      "3.39497\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4a68b60c50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/home/evan/Documents/deep_docking/datasets/v2015/4a9n/4a9n_ligand.mol2\n",
      "[[ 1.514   1.9077 -1.4783]\n",
      " [ 0.174   1.7917 -1.9773]\n",
      " [-0.051   1.5367 -3.3643]\n",
      " [-0.589   3.1577 -1.6003]]\n",
      "Butane:\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4a6a657110>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0496101379395\n",
      "Training epoch 35000\n",
      "Iter: 35000\n",
      "D loss: 29.05\n",
      "G_loss: 412.1\n",
      "4wks:\n",
      "\n",
      "real:\n",
      "[[ 1.514   1.9077 -1.4783]\n",
      " [ 0.174   1.7917 -1.9773]\n",
      " [-0.051   1.5367 -3.3643]\n",
      " [-0.589   3.1577 -1.6003]]\n",
      "fake:\n",
      "[[ 1.514   1.9077 -1.4783]\n",
      " [ 0.174   1.7917 -1.9773]\n",
      " [-0.051   1.5367 -3.3643]\n",
      " [-0.589   3.1577 -1.6003]]\n",
      "fake:\n",
      "[[ 1.  1.  1.]\n",
      " [ 1.  1.  1.]\n",
      " [ 1.  1.  1.]]\n",
      "-0.912921\n",
      "3.04546\n"
     ]
    },
    {
     "data": {
      "image/png": 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6EeDCbno98BRwxVCbncCjXdsrgKe65euA/wB+C3gr8Mzw12At9GM1jcc8+vIbwO8Bfwt8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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4a697be090>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/home/evan/Documents/deep_docking/datasets/v2015/4a9n/4a9n_ligand.mol2\n",
      "[[ 1.514   1.9077 -1.4783]\n",
      " [ 0.174   1.7917 -1.9773]\n",
      " [-0.051   1.5367 -3.3643]\n",
      " [-0.589   3.1577 -1.6003]]\n",
      "Butane:\n",
      "\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4a17848f10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0397350788116\n",
      "Training epoch 36000\n",
      "Iter: 36000\n",
      "D loss: 26.95\n",
      "G_loss: 433.7\n",
      "4wks:\n",
      "\n",
      "real:\n",
      "[[ 1.514   1.9077 -1.4783]\n",
      " [ 0.174   1.7917 -1.9773]\n",
      " [-0.051   1.5367 -3.3643]\n",
      " [-0.589   3.1577 -1.6003]]\n",
      "fake:\n",
      "[[ 1.514   1.9077 -1.4783]\n",
      " [ 0.174   1.7917 -1.9773]\n",
      " [-0.051   1.5367 -3.3643]\n",
      " [-0.589   3.1577 -1.6003]]\n",
      "fake:\n",
      "[[ 1.  1.  1.]\n",
      " [ 1.  1.  1.]\n",
      " [ 1.  1.  1.]]\n",
      "-0.912921\n",
      "3.14683\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4a67ed5bd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/home/evan/Documents/deep_docking/datasets/v2015/4a9n/4a9n_ligand.mol2\n",
      "[[ 1.514   1.9077 -1.4783]\n",
      " [ 0.174   1.7917 -1.9773]\n",
      " [-0.051   1.5367 -3.3643]\n",
      " [-0.589   3.1577 -1.6003]]\n",
      "Butane:\n",
      "\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4a71722850>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0418510437012\n",
      "Training epoch 37000\n",
      "Iter: 37000\n",
      "D loss: 20.53\n",
      "G_loss: 477.0\n",
      "4wks:\n",
      "\n",
      "real:\n",
      "[[ 1.514   1.9077 -1.4783]\n",
      " [ 0.174   1.7917 -1.9773]\n",
      " [-0.051   1.5367 -3.3643]\n",
      " [-0.589   3.1577 -1.6003]]\n",
      "fake:\n",
      "[[ 1.514   1.9077 -1.4783]\n",
      " [ 0.174   1.7917 -1.9773]\n",
      " [-0.051   1.5367 -3.3643]\n",
      " [-0.589   3.1577 -1.6003]]\n",
      "fake:\n",
      "[[ 1.  1.  1.]\n",
      " [ 1.  1.  1.]\n",
      " [ 1.  1.  1.]]\n",
      "-0.912921\n",
      "3.24569\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAW4AAAD8CAYAAABXe05zAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4a7172ee10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/home/evan/Documents/deep_docking/datasets/v2015/4a9n/4a9n_ligand.mol2\n",
      "[[ 1.514   1.9077 -1.4783]\n",
      " [ 0.174   1.7917 -1.9773]\n",
      " [-0.051   1.5367 -3.3643]\n",
      " [-0.589   3.1577 -1.6003]]\n",
      "Butane:\n",
      "\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4a0fcf7b90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0386190414429\n",
      "Training epoch 38000\n",
      "Iter: 38000\n",
      "D loss: 17.21\n",
      "G_loss: 494.1\n",
      "4wks:\n",
      "\n",
      "real:\n",
      "[[ 1.514   1.9077 -1.4783]\n",
      " [ 0.174   1.7917 -1.9773]\n",
      " [-0.051   1.5367 -3.3643]\n",
      " [-0.589   3.1577 -1.6003]]\n",
      "fake:\n",
      "[[ 1.514   1.9077 -1.4783]\n",
      " [ 0.174   1.7917 -1.9773]\n",
      " [-0.051   1.5367 -3.3643]\n",
      " [-0.589   3.1577 -1.6003]]\n",
      "fake:\n",
      "[[ 1.  1.  1.]\n",
      " [ 1.  1.  1.]\n",
      " [ 1.  1.  1.]]\n",
      "-0.912921\n",
      "3.18578\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4a7179fcd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/home/evan/Documents/deep_docking/datasets/v2015/4a9n/4a9n_ligand.mol2\n",
      "[[ 1.514   1.9077 -1.4783]\n",
      " [ 0.174   1.7917 -1.9773]\n",
      " [-0.051   1.5367 -3.3643]\n",
      " [-0.589   3.1577 -1.6003]]\n",
      "Butane:\n",
      "\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4a68a25710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0372970104218\n",
      "Training epoch 39000\n",
      "Iter: 39000\n",
      "D loss: 13.3\n",
      "G_loss: 499.5\n",
      "4wks:\n",
      "\n",
      "real:\n",
      "[[ 1.514   1.9077 -1.4783]\n",
      " [ 0.174   1.7917 -1.9773]\n",
      " [-0.051   1.5367 -3.3643]\n",
      " [-0.589   3.1577 -1.6003]]\n",
      "fake:\n",
      "[[ 1.514   1.9077 -1.4783]\n",
      " [ 0.174   1.7917 -1.9773]\n",
      " [-0.051   1.5367 -3.3643]\n",
      " [-0.589   3.1577 -1.6003]]\n",
      "fake:\n",
      "[[ 1.  1.  1.]\n",
      " [ 1.  1.  1.]\n",
      " [ 1.  1.  1.]]\n",
      "-0.912921\n",
      "3.8302\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4a15bc8350>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/home/evan/Documents/deep_docking/datasets/v2015/4a9n/4a9n_ligand.mol2\n",
      "[[ 1.514   1.9077 -1.4783]\n",
      " [ 0.174   1.7917 -1.9773]\n",
      " [-0.051   1.5367 -3.3643]\n",
      " [-0.589   3.1577 -1.6003]]\n",
      "Butane:\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4a7132fed0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.047278881073\n",
      "Training epoch 40000\n",
      "Iter: 40000\n",
      "D loss: 11.26\n",
      "G_loss: 523.2\n",
      "4wks:\n",
      "\n",
      "real:\n",
      "[[ 1.514   1.9077 -1.4783]\n",
      " [ 0.174   1.7917 -1.9773]\n",
      " [-0.051   1.5367 -3.3643]\n",
      " [-0.589   3.1577 -1.6003]]\n",
      "fake:\n",
      "[[ 1.514   1.9077 -1.4783]\n",
      " [ 0.174   1.7917 -1.9773]\n",
      " [-0.051   1.5367 -3.3643]\n",
      " [-0.589   3.1577 -1.6003]]\n",
      "fake:\n",
      "[[ 1.  1.  1.]\n",
      " [ 1.  1.  1.]\n",
      " [ 1.  1.  1.]]\n",
      "-0.912921\n",
      "3.78915\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4a679caf90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/home/evan/Documents/deep_docking/datasets/v2015/4a9n/4a9n_ligand.mol2\n",
      "[[ 1.514   1.9077 -1.4783]\n",
      " [ 0.174   1.7917 -1.9773]\n",
      " [-0.051   1.5367 -3.3643]\n",
      " [-0.589   3.1577 -1.6003]]\n",
      "Butane:\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAW4AAAD8CAYAAABXe05zAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nSwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4a0ea32f90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0412428379059\n",
      "Training epoch 41000\n",
      "Iter: 41000\n",
      "D loss: 12.48\n",
      "G_loss: 484.1\n",
      "4wks:\n",
      "\n",
      "real:\n",
      "[[ 1.514   1.9077 -1.4783]\n",
      " [ 0.174   1.7917 -1.9773]\n",
      " [-0.051   1.5367 -3.3643]\n",
      " [-0.589   3.1577 -1.6003]]\n",
      "fake:\n",
      "[[ 1.514   1.9077 -1.4783]\n",
      " [ 0.174   1.7917 -1.9773]\n",
      " [-0.051   1.5367 -3.3643]\n",
      " [-0.589   3.1577 -1.6003]]\n",
      "fake:\n",
      "[[ 1.  1.  1.]\n",
      " [ 1.  1.  1.]\n",
      " [ 1.  1.  1.]]\n",
      "-0.912921\n",
      "3.64369\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4a0eff7b90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/home/evan/Documents/deep_docking/datasets/v2015/4a9n/4a9n_ligand.mol2\n",
      "[[ 1.514   1.9077 -1.4783]\n",
      " [ 0.174   1.7917 -1.9773]\n",
      " [-0.051   1.5367 -3.3643]\n",
      " [-0.589   3.1577 -1.6003]]\n",
      "Butane:\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4a68226890>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.041424036026\n",
      "Training epoch 42000\n",
      "Iter: 42000\n",
      "D loss: 16.21\n",
      "G_loss: 453.7\n",
      "4wks:\n",
      "\n",
      "real:\n",
      "[[ 1.514   1.9077 -1.4783]\n",
      " [ 0.174   1.7917 -1.9773]\n",
      " [-0.051   1.5367 -3.3643]\n",
      " [-0.589   3.1577 -1.6003]]\n",
      "fake:\n",
      "[[ 1.514   1.9077 -1.4783]\n",
      " [ 0.174   1.7917 -1.9773]\n",
      " [-0.051   1.5367 -3.3643]\n",
      " [-0.589   3.1577 -1.6003]]\n",
      "fake:\n",
      "[[ 1.  1.  1.]\n",
      " [ 1.  1.  1.]\n",
      " [ 1.  1.  1.]]\n",
      "-0.912921\n",
      "3.37845\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4a0e52fa90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/home/evan/Documents/deep_docking/datasets/v2015/4a9n/4a9n_ligand.mol2\n",
      "[[ 1.514   1.9077 -1.4783]\n",
      " [ 0.174   1.7917 -1.9773]\n",
      " [-0.051   1.5367 -3.3643]\n",
      " [-0.589   3.1577 -1.6003]]\n",
      "Butane:\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4a69a7d650>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0447158813477\n",
      "Training epoch 43000\n",
      "Iter: 43000\n",
      "D loss: 21.09\n",
      "G_loss: 488.7\n",
      "4wks:\n",
      "\n",
      "real:\n",
      "[[ 1.514   1.9077 -1.4783]\n",
      " [ 0.174   1.7917 -1.9773]\n",
      " [-0.051   1.5367 -3.3643]\n",
      " [-0.589   3.1577 -1.6003]]\n",
      "fake:\n",
      "[[ 1.514   1.9077 -1.4783]\n",
      " [ 0.174   1.7917 -1.9773]\n",
      " [-0.051   1.5367 -3.3643]\n",
      " [-0.589   3.1577 -1.6003]]\n",
      "fake:\n",
      "[[ 1.  1.  1.]\n",
      " [ 1.  1.  1.]\n",
      " [ 1.  1.  1.]]\n",
      "-0.912921\n",
      "3.29136\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4a15787910>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/home/evan/Documents/deep_docking/datasets/v2015/4a9n/4a9n_ligand.mol2\n",
      "[[ 1.514   1.9077 -1.4783]\n",
      " [ 0.174   1.7917 -1.9773]\n",
      " [-0.051   1.5367 -3.3643]\n",
      " [-0.589   3.1577 -1.6003]]\n",
      "Butane:\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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K96r6j6q60i0eY/Z++yb17GufV0eseVX1VFWN+4G3NaFnX1s4r3uB+7v5+4G/WcVaxmHV\nX9vSVLjP80/A0UW2FfD1JCe6p2bXu8X6+nJ7LURr53UxLZzX1895FuZ/gdcv0m69ntNVf23Lir5+\nYBSSfB24ZoFNH62qf+/afBS4Anxukd38eVVdSPJHwMNJnq6qPr/yr6gR9XVd6NPXHpo5ry24Wj/n\nLlRVJVnstr11cU7XonUX7lX1V1fbnuQfgVuAv6xF7vOsqgvdv88m+Qqzv0KtuR+YEfS112sh1oJB\nfe25jybOaw/r4rxerZ9JfpxkS1Vd7IYinl1kH+vinC5gZK9tWaqmhmWS3Ah8CHhXVb24SJvfT/La\n38wDf83sH7HWlT59pd+rI5rQynntqYXzehh4bzf/XuB3fmNZ5+d09V/bUlXNTMy+/uAccKqbZrr1\nfwwc6eb/FPhuN51h9lfhVa99HH3tlvcwezfND9dxX/+W2fHIXwA/Br7W8Hkd2NcWzivwh8zeJfMD\n4OvA61o7pwudI2A/sL+bD7N31PwQeAKYGuXxfUJVkhrU1LCMJGmW4S5JDTLcJalBhrskNchwl6QG\nGe6S1CDDXZIaZLhLUoP+D277jxhpdUnnAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4a15fb4cd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0710680484772\n",
      "Training epoch 44000\n",
      "Iter: 44000\n",
      "D loss: 14.14\n",
      "G_loss: 484.7\n",
      "4wks:\n",
      "\n",
      "real:\n",
      "[[ 1.514   1.9077 -1.4783]\n",
      " [ 0.174   1.7917 -1.9773]\n",
      " [-0.051   1.5367 -3.3643]\n",
      " [-0.589   3.1577 -1.6003]]\n",
      "fake:\n",
      "[[ 1.514   1.9077 -1.4783]\n",
      " [ 0.174   1.7917 -1.9773]\n",
      " [-0.051   1.5367 -3.3643]\n",
      " [-0.589   3.1577 -1.6003]]\n",
      "fake:\n",
      "[[ 1.  1.  1.]\n",
      " [ 1.  1.  1.]\n",
      " [ 1.  1.  1.]]\n",
      "-0.912921\n",
      "2.48531\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4a15787810>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/home/evan/Documents/deep_docking/datasets/v2015/4a9n/4a9n_ligand.mol2\n",
      "[[ 1.514   1.9077 -1.4783]\n",
      " [ 0.174   1.7917 -1.9773]\n",
      " [-0.051   1.5367 -3.3643]\n",
      " [-0.589   3.1577 -1.6003]]\n",
      "Butane:\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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kfR4l0ap/Aj4ErK2zmeU3alzW3THzy2DvvIE1esys1XD/6+6/Tfcm+c15agr4ZpKj3Ttj\n14NR47IuHxORZDdwpqqeHFG6ro6ZnuOyXo+Zv03yPPDnwJ3zlK3q4+WSPn6gryTfBC4fsuqjzD5x\n8pPMDuwngX8E/nJI7R9V1ZkkbwYeTvJcVX17uXq+FMY0Lk0aMTYfAf6kx2bW2zHTd1yac7Fxqap/\nr6qPAh9N8mHgfcDHhtSu6uNlVYZ7Vf1xn7ok/wI8MM82znT/vpDk68z+93LVDPxijGFcej0mYi2a\nb2yS/AHwFuDJJDD7PR9LsqOq/ndgG+vmmFnAuDR5zPT9XQK+yOx7fH4t3Ff78bLmLssMPEr4z4Dv\nDql5Q5I3/XKa2bOTX6trSZ9xod+jJJpSVU9V1Zuraqqqppi9rHDVYLCvt2Om77iwDo+ZJNvnzO4G\nnhtSs+qPl1V55j7C3yd5B7OXH04BfwXQ3a702araBfwW8PXujOQy4N+q6j9Wpt1LZuS4zPcoiZVq\neKV5zAznMcNdSX4P+AXwA2AvrL3jZc3dCilJGm3NXZaRJI1muEtSgwx3SWqQ4S5JDTLcJalBhrsk\nNchwl6QGGe6S1KD/A4ky7Bl9qX1+AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4a0e05d1d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0400800704956\n",
      "Training epoch 45000\n",
      "Iter: 45000\n",
      "D loss: 10.71\n",
      "G_loss: 464.6\n",
      "4wks:\n",
      "\n",
      "real:\n",
      "[[ 1.514   1.9077 -1.4783]\n",
      " [ 0.174   1.7917 -1.9773]\n",
      " [-0.051   1.5367 -3.3643]\n",
      " [-0.589   3.1577 -1.6003]]\n",
      "fake:\n",
      "[[ 1.514   1.9077 -1.4783]\n",
      " [ 0.174   1.7917 -1.9773]\n",
      " [-0.051   1.5367 -3.3643]\n",
      " [-0.589   3.1577 -1.6003]]\n",
      "fake:\n",
      "[[ 1.  1.  1.]\n",
      " [ 1.  1.  1.]\n",
      " [ 1.  1.  1.]]\n",
      "-0.912921\n",
      "2.62931\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4a0e269650>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/home/evan/Documents/deep_docking/datasets/v2015/4a9n/4a9n_ligand.mol2\n",
      "[[ 1.514   1.9077 -1.4783]\n",
      " [ 0.174   1.7917 -1.9773]\n",
      " [-0.051   1.5367 -3.3643]\n",
      " [-0.589   3.1577 -1.6003]]\n",
      "Butane:\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXAAAAD8CAYAAABuHP8oAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4a15853810>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0486690998077\n",
      "Training epoch 46000\n",
      "Iter: 46000\n",
      "D loss: 10.01\n",
      "G_loss: 488.9\n",
      "4wks:\n",
      "\n",
      "real:\n",
      "[[ 1.514   1.9077 -1.4783]\n",
      " [ 0.174   1.7917 -1.9773]\n",
      " [-0.051   1.5367 -3.3643]\n",
      " [-0.589   3.1577 -1.6003]]\n",
      "fake:\n",
      "[[ 1.514   1.9077 -1.4783]\n",
      " [ 0.174   1.7917 -1.9773]\n",
      " [-0.051   1.5367 -3.3643]\n",
      " [-0.589   3.1577 -1.6003]]\n",
      "fake:\n",
      "[[ 1.  1.  1.]\n",
      " [ 1.  1.  1.]\n",
      " [ 1.  1.  1.]]\n",
      "-0.912921\n",
      "2.73721\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4a6a535e10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/home/evan/Documents/deep_docking/datasets/v2015/4a9n/4a9n_ligand.mol2\n",
      "[[ 1.514   1.9077 -1.4783]\n",
      " [ 0.174   1.7917 -1.9773]\n",
      " [-0.051   1.5367 -3.3643]\n",
      " [-0.589   3.1577 -1.6003]]\n",
      "Butane:\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4a0fa5b890>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0406711101532\n",
      "Training epoch 47000\n",
      "Iter: 47000\n",
      "D loss: 8.629\n",
      "G_loss: 469.8\n",
      "4wks:\n",
      "\n",
      "real:\n",
      "[[ 1.514   1.9077 -1.4783]\n",
      " [ 0.174   1.7917 -1.9773]\n",
      " [-0.051   1.5367 -3.3643]\n",
      " [-0.589   3.1577 -1.6003]]\n",
      "fake:\n",
      "[[ 1.514   1.9077 -1.4783]\n",
      " [ 0.174   1.7917 -1.9773]\n",
      " [-0.051   1.5367 -3.3643]\n",
      " [-0.589   3.1577 -1.6003]]\n",
      "fake:\n",
      "[[ 1.  1.  1.]\n",
      " [ 1.  1.  1.]\n",
      " [ 1.  1.  1.]]\n",
      "-0.912921\n",
      "2.63958\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4a70b10d50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/home/evan/Documents/deep_docking/datasets/v2015/4a9n/4a9n_ligand.mol2\n",
      "[[ 1.514   1.9077 -1.4783]\n",
      " [ 0.174   1.7917 -1.9773]\n",
      " [-0.051   1.5367 -3.3643]\n",
      " [-0.589   3.1577 -1.6003]]\n",
      "Butane:\n",
      "\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4a6f313850>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0407001972198\n",
      "Training epoch 48000\n",
      "Iter: 48000\n",
      "D loss: 21.56\n",
      "G_loss: 495.9\n",
      "4wks:\n",
      "\n",
      "real:\n",
      "[[ 1.514   1.9077 -1.4783]\n",
      " [ 0.174   1.7917 -1.9773]\n",
      " [-0.051   1.5367 -3.3643]\n",
      " [-0.589   3.1577 -1.6003]]\n",
      "fake:\n",
      "[[ 1.514   1.9077 -1.4783]\n",
      " [ 0.174   1.7917 -1.9773]\n",
      " [-0.051   1.5367 -3.3643]\n",
      " [-0.589   3.1577 -1.6003]]\n",
      "fake:\n",
      "[[ 1.  1.  1.]\n",
      " [ 1.  1.  1.]\n",
      " [ 1.  1.  1.]]\n",
      "-0.912921\n",
      "2.61457\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4a0eb4b210>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/home/evan/Documents/deep_docking/datasets/v2015/4a9n/4a9n_ligand.mol2\n",
      "[[ 1.514   1.9077 -1.4783]\n",
      " [ 0.174   1.7917 -1.9773]\n",
      " [-0.051   1.5367 -3.3643]\n",
      " [-0.589   3.1577 -1.6003]]\n",
      "Butane:\n",
      "\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4a153bbf50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0395050048828\n",
      "Training epoch 49000\n",
      "Iter: 49000\n",
      "D loss: 11.08\n",
      "G_loss: 498.0\n",
      "4wks:\n",
      "\n",
      "real:\n",
      "[[ 1.514   1.9077 -1.4783]\n",
      " [ 0.174   1.7917 -1.9773]\n",
      " [-0.051   1.5367 -3.3643]\n",
      " [-0.589   3.1577 -1.6003]]\n",
      "fake:\n",
      "[[ 1.514   1.9077 -1.4783]\n",
      " [ 0.174   1.7917 -1.9773]\n",
      " [-0.051   1.5367 -3.3643]\n",
      " [-0.589   3.1577 -1.6003]]\n",
      "fake:\n",
      "[[ 1.  1.  1.]\n",
      " [ 1.  1.  1.]\n",
      " [ 1.  1.  1.]]\n",
      "-0.912921\n",
      "3.07381\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAW4AAAD8CAYAAABXe05zAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAADT9JREFUeJzt3X+s3fVdx/HXy7a4CWxd1jPEwd2d0cDIsgFeEWUxGQQt\nqxlTlwhRFnHLjX9sgcTFdf6hWcySGpNFzfyRhs3NiFuWrU0mZVvQMcmy0a2F8qO0LIjFgWiLOIFp\nxgov/zhfZrnce8/ntPd7znm3z0dywzk9H27fn97e5/3e7/2eUycRAKCOH5r2AACA8RBuACiGcANA\nMYQbAIoh3ABQDOEGgGIINwAUQ7gBoBjCDQDFrO/jnW7atCnz8/N9vGsAOCnt3bv3iSSDlrW9hHt+\nfl579uzp410DwEnJ9iOtazlVAgDFEG4AKIZwA0AxhBsAiiHcAFDMyHDbPs/2vmPenrJ94ySGAwC8\n1MjLAZM8KOlCSbK9TtJjknb2PBcAYAXjniq5QtI/J2m+3hAAsLbGDfc1kj7VxyAAgDbNz5y0fZqk\nt0v64AqPL0palKS5ubk1GQ6YlPmtu8Zaf2jblp4mAUYb54j7Kkl3JfmP5R5Msj3JQpKFwaDp6fYA\ngOMwTrivFadJAGDqmsJt+3RJV0ra0e84AIBRms5xJ/mupFf3PAsAoAHPnASAYgg3ABRDuAGgGMIN\nAMUQbgAohnADQDGEGwCKIdwAUAzhBoBiCDcAFEO4AaAYwg0AxRBuACiGcANAMYQbAIoh3ABQDOEG\ngGIINwAUQ7gBoBjCDQDFEG4AKKYp3LY32v6s7YO2D9j+2b4HAwAsb33juj+V9MUk77R9mqQf6XEm\nAMAqRobb9isl/byk35SkJM9KerbfsQAAK2k5VfJ6SUck/bXtu23fZPv0pYtsL9reY3vPkSNH1nxQ\nAMBQS7jXS7pY0l8muUjSdyVtXbooyfYkC0kWBoPBGo8JAHhBS7gflfRokt3d/c9qGHIAwBSMDHeS\nf5f0bdvndb90haQHep0KALCi1qtK3ifp5u6KkoclXd/fSACA1TSFO8k+SQs9zwIAaMAzJwGgGMIN\nAMUQbgAohnADQDGEGwCKIdwAUAzhBoBiCDcAFEO4AaAYwg0AxRBuACiGcANAMYQbAIoh3ABQDOEG\ngGIINwAUQ7gBoBjCDQDFEG4AKIZwA0AxTf9YsO1Dkp6W9Jyko0n4h4MBYEqawt15a5InepsEANCE\nUyUAUExruCPpH2zvtb3Y50AAgNW1nip5S5LHbL9G0m22Dya549gFXdAXJWlubm6NxwROPfNbd421\n/tC2LT1NglnTdMSd5LHuv4cl7ZR0yTJrtidZSLIwGAzWdkoAwA+MDLft022f+cJtSb8g6f6+BwMA\nLK/lVMlZknbafmH93yX5Yq9TAQBWNDLcSR6W9OYJzAIAaMDlgABQDOEGgGIINwAUQ7gBoBjCDQDF\nEG4AKIZwA0AxhBsAiiHcAFAM4QaAYgg3ABRDuAGgGMINAMUQbgAohnADQDGEGwCKIdwAUAzhBoBi\nCDcAFEO4AaAYwg0AxTSH2/Y623fbvqXPgQAAqxvniPsGSQf6GgQA0KYp3LbPkbRF0k39jgMAGKX1\niPtPJP2upOdXWmB70fYe23uOHDmyJsMBAF5qZLht/5Kkw0n2rrYuyfYkC0kWBoPBmg0IAHixliPu\nyyS93fYhSZ+WdLntv+11KgDAikaGO8kHk5yTZF7SNZK+nOQ3ep8MALAsruMGgGLWj7M4yVckfaWX\nSQAATTjiBoBiCDcAFEO4AaAYwg0AxRBuACiGcANAMYQbAIoh3ABQDOEGgGIINwAUQ7gBoBjCDQDF\nEG4AKIZwA0AxhBsAiiHcAFAM4QaAYgg3ABRDuAGgGMINAMWMDLftl9n+hu17bO+3/aFJDAYAWF7L\nv/L+PUmXJ3nG9gZJX7X9hSR39jwbAGAZI8OdJJKe6e5u6N7S51AAgJU1neO2vc72PkmHJd2WZHe/\nYwEAVtJyqkRJnpN0oe2NknbafmOS+49dY3tR0qIkzc3NrfmgwDjmt+6a9ggvMmvzoLaxripJ8h1J\nt0vavMxj25MsJFkYDAZrNR8AYImWq0oG3ZG2bL9c0pWSDvY9GABgeS2nSs6W9Enb6zQM/WeS3NLv\nWACAlbRcVXKvpIsmMAsAoAHPnASAYgg3ABRDuAGgGMINAMUQbgAohnADQDGEGwCKIdwAUAzhBoBi\nCDcAFEO4AaAYwg0AxRBuACiGcANAMYQbAIoh3ABQDOEGgGIINwAUQ7gBoBjCDQDFEG4AKGZkuG2f\na/t22w/Y3m/7hkkMBgBY3vqGNUcl/U6Su2yfKWmv7duSPNDzbACAZYw84k7yeJK7uttPSzog6bV9\nDwYAWN5Y57htz0u6SNLuPoYBAIzmJG0L7TMk/ZOkDyfZsczji5IWJWlubu6nHnnkkbWcEzNsfuuu\nsf+fQ9u29P57YHXjfgzQL9t7kyy0rG064ra9QdLnJN28XLQlKcn2JAtJFgaDQfu0AICxtFxVYkkf\nk3QgyUf6HwkAsJqWI+7LJF0n6XLb+7q3t/U8FwBgBSMvB0zyVUmewCwAgAY8cxIAiiHcAFAM4QaA\nYgg3ABRDuAGgGMINAMUQbgAohnADQDGEGwCKIdwAUAzhBoBiCDcAFEO4AaAYwg0AxRBuACiGcANA\nMYQbAIoh3ABQDOEGgGIINwAUQ7gBoJiR4bb9cduHbd8/iYEAAKtrOeL+hKTNPc8BAGg0MtxJ7pD0\n5ARmAQA0WL9W78j2oqRFSZqbmzvu9zO/dddY6w9t23Lcv1eLWZvnZDHunyumr++P2Sx+7szq5/+a\n/XAyyfYkC0kWBoPBWr1bAMASXFUCAMUQbgAopuVywE9J+rqk82w/avvd/Y8FAFjJyB9OJrl2EoMA\nANpwqgQAiiHcAFAM4QaAYgg3ABRDuAGgGMINAMUQbgAohnADQDGEGwCKIdwAUAzhBoBiCDcAFEO4\nAaAYwg0AxRBuACiGcANAMYQbAIoh3ABQDOEGgGIINwAU0xRu25ttP2j7Idtb+x4KALCykeG2vU7S\nn0u6StIFkq61fUHfgwEAltdyxH2JpIeSPJzkWUmflnR1v2MBAFbSEu7XSvr2Mfcf7X4NADAFTrL6\nAvudkjYneU93/zpJP5PkvUvWLUpa7O6eJ+nBtR+3V5skPTHtIdYA+5gdJ8MeJPYxKa9LMmhZuL5h\nzWOSzj3m/jndr71Iku2StjeNN4Ns70myMO05ThT7mB0nwx4k9jGLWk6VfFPST9p+ve3TJF0j6fP9\njgUAWMnII+4kR22/V9KXJK2T9PEk+3ufDACwrJZTJUpyq6Rbe55l2sqe5lmCfcyOk2EPEvuYOSN/\nOAkAmC085R0Aijmlwm37XNu3237A9n7bNyyz5pW2/972Pd2a66cx62oa9/Eq2ztt32v7G7bfOI1Z\nV2L7Zd1cL/w5f2iZNbb9Z91LLdxr++JpzLqaxn2cb/vrtr9n+/3TmHOUxn38evdxuM/212y/eRqz\nrqZxH1d3+9hne4/tt0xj1hOS5JR5k3S2pIu722dK+pakC5as+T1Jf9TdHkh6UtJp0579OPbxx5L+\noLt9vqR/nPbcS+azpDO62xsk7ZZ06ZI1b5P0hW7tpZJ2T3vu49zHayT9tKQPS3r/tGc+gX38nKRX\ndbevKvzxOEP/f5r4TZIOTnvucd9OqSPuJI8nuau7/bSkA3rps0Aj6Uzb1vAD/KSkoxMddITGfVwg\n6cvdmoOS5m2fNdFBV5GhZ7q7G7q3pT9wuVrS33Rr75S00fbZk5xzlJZ9JDmc5JuSvj/p+Vo17uNr\nSf6ru3unhs/pmCmN+3gmXbUlnb708QpOqXAfy/a8pIs0/Ip8rI9KeoOkf5N0n6Qbkjw/0eHGsMo+\n7pH0K92aSyS9TjP2iWZ7ne19kg5Lui3J0j2UeLmFhn2UMOY+3q3hd0Mzp2Uftn/Z9kFJuyT91qRn\nPFGnZLhtnyHpc5JuTPLUkod/UdI+ST8m6UJJH7X9igmP2GTEPrZpeIS6T9L7JN0t6bkJj7iqJM8l\nuVDDLyiXzNp5+Fan2j5sv1XDcH9gkvO1atlHkp1Jzpf0Dkl/OOkZT9QpF27bGzSM3c1Jdiyz5HpJ\nO7pvuR6S9C8aniOeKaP2keSpJNd3f4HfpeH5+ocnPGaTJN+RdLukzUseanq5hVmxyj5KWW0ftt8k\n6SZJVyf5z0nPNo6Wj0eSOyT9uO1NExtsDZxS4e7OW39M0oEkH1lh2b9KuqJbf5aGL5g1U8Fr2Yft\njd1LFEjSeyTdscxR+dTYHtje2N1+uaQrJR1csuzzkt7VXV1yqaT/TvL4hEddVeM+Zl7LPmzPSdoh\n6bok35r8lKM17uMnus8hdVcq/bCkmf4itFTTMydPIpdJuk7Sfd0pBGl4FcmcJCX5Kw2/bfqE7fs0\n/An1B5LM2iuKtezjDZI+aTuS9mv4re0sOVvD+dZpeADxmSS32P5t6Qd7uFXDK0sekvQ/Gn43NGtG\n7sP2j0raI+kVkp63faOGVwHNzBdStX08fl/SqyX9Rde9o5m9F21q2cevanhA8H1J/yvp1475YWUJ\nPHMSAIo5pU6VAMDJgHADQDGEGwCKIdwAUAzhBoBiCDcAFEO4AaAYwg0AxfwfDNM4mI5mgDsAAAAA\nSUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4a6f8c2dd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/home/evan/Documents/deep_docking/datasets/v2015/4a9n/4a9n_ligand.mol2\n",
      "[[ 1.514   1.9077 -1.4783]\n",
      " [ 0.174   1.7917 -1.9773]\n",
      " [-0.051   1.5367 -3.3643]\n",
      " [-0.589   3.1577 -1.6003]]\n",
      "Butane:\n",
      "\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4a16ddbb10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0417008399963\n",
      "Training epoch 50000\n",
      "Iter: 50000\n",
      "D loss: 13.13\n",
      "G_loss: 443.2\n",
      "4wks:\n",
      "\n",
      "real:\n",
      "[[ 1.514   1.9077 -1.4783]\n",
      " [ 0.174   1.7917 -1.9773]\n",
      " [-0.051   1.5367 -3.3643]\n",
      " [-0.589   3.1577 -1.6003]]\n",
      "fake:\n",
      "[[ 1.514   1.9077 -1.4783]\n",
      " [ 0.174   1.7917 -1.9773]\n",
      " [-0.051   1.5367 -3.3643]\n",
      " [-0.589   3.1577 -1.6003]]\n",
      "fake:\n",
      "[[ 1.  1.  1.]\n",
      " [ 1.  1.  1.]\n",
      " [ 1.  1.  1.]]\n",
      "-0.912921\n",
      "2.97036\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAW4AAAD8CAYAAABXe05zAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4a0f6c3090>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/home/evan/Documents/deep_docking/datasets/v2015/4a9n/4a9n_ligand.mol2\n",
      "[[ 1.514   1.9077 -1.4783]\n",
      " [ 0.174   1.7917 -1.9773]\n",
      " [-0.051   1.5367 -3.3643]\n",
      " [-0.589   3.1577 -1.6003]]\n",
      "Butane:\n",
      "\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4a16819490>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0610220432281\n",
      "Training epoch 51000\n",
      "Iter: 51000\n",
      "D loss: 10.81\n",
      "G_loss: 500.2\n",
      "4wks:\n",
      "\n",
      "real:\n",
      "[[ 1.514   1.9077 -1.4783]\n",
      " [ 0.174   1.7917 -1.9773]\n",
      " [-0.051   1.5367 -3.3643]\n",
      " [-0.589   3.1577 -1.6003]]\n",
      "fake:\n",
      "[[ 1.514   1.9077 -1.4783]\n",
      " [ 0.174   1.7917 -1.9773]\n",
      " [-0.051   1.5367 -3.3643]\n",
      " [-0.589   3.1577 -1.6003]]\n",
      "fake:\n",
      "[[ 1.  1.  1.]\n",
      " [ 1.  1.  1.]\n",
      " [ 1.  1.  1.]]\n",
      "-0.912921\n",
      "2.83321\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4a0ff672d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/home/evan/Documents/deep_docking/datasets/v2015/4a9n/4a9n_ligand.mol2\n",
      "[[ 1.514   1.9077 -1.4783]\n",
      " [ 0.174   1.7917 -1.9773]\n",
      " [-0.051   1.5367 -3.3643]\n",
      " [-0.589   3.1577 -1.6003]]\n",
      "Butane:\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4a687e0550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.03946185112\n",
      "Training epoch 52000\n",
      "Iter: 52000\n",
      "D loss: 11.67\n",
      "G_loss: 486.8\n",
      "4wks:\n",
      "\n",
      "real:\n",
      "[[ 1.514   1.9077 -1.4783]\n",
      " [ 0.174   1.7917 -1.9773]\n",
      " [-0.051   1.5367 -3.3643]\n",
      " [-0.589   3.1577 -1.6003]]\n",
      "fake:\n",
      "[[ 1.514   1.9077 -1.4783]\n",
      " [ 0.174   1.7917 -1.9773]\n",
      " [-0.051   1.5367 -3.3643]\n",
      " [-0.589   3.1577 -1.6003]]\n",
      "fake:\n",
      "[[ 1.  1.  1.]\n",
      " [ 1.  1.  1.]\n",
      " [ 1.  1.  1.]]\n",
      "-0.912921\n",
      "2.56722\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4a676bebd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/home/evan/Documents/deep_docking/datasets/v2015/4a9n/4a9n_ligand.mol2\n",
      "[[ 1.514   1.9077 -1.4783]\n",
      " [ 0.174   1.7917 -1.9773]\n",
      " [-0.051   1.5367 -3.3643]\n",
      " [-0.589   3.1577 -1.6003]]\n",
      "Butane:\n",
      "\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4a7068c690>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0401091575623\n",
      "Training epoch 53000\n",
      "Iter: 53000\n",
      "D loss: 14.38\n",
      "G_loss: 483.3\n",
      "4wks:\n",
      "\n",
      "real:\n",
      "[[ 1.514   1.9077 -1.4783]\n",
      " [ 0.174   1.7917 -1.9773]\n",
      " [-0.051   1.5367 -3.3643]\n",
      " [-0.589   3.1577 -1.6003]]\n",
      "fake:\n",
      "[[ 1.514   1.9077 -1.4783]\n",
      " [ 0.174   1.7917 -1.9773]\n",
      " [-0.051   1.5367 -3.3643]\n",
      " [-0.589   3.1577 -1.6003]]\n",
      "fake:\n",
      "[[ 1.  1.  1.]\n",
      " [ 1.  1.  1.]\n",
      " [ 1.  1.  1.]]\n",
      "-0.912921\n",
      "2.74393\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4a697755d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/home/evan/Documents/deep_docking/datasets/v2015/4a9n/4a9n_ligand.mol2\n",
      "[[ 1.514   1.9077 -1.4783]\n",
      " [ 0.174   1.7917 -1.9773]\n",
      " [-0.051   1.5367 -3.3643]\n",
      " [-0.589   3.1577 -1.6003]]\n",
      "Butane:\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAW4AAAD8CAYAAABXe05zAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4a6c58e150>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.042445898056\n",
      "Training epoch 54000\n",
      "Iter: 54000\n",
      "D loss: 12.67\n",
      "G_loss: 531.2\n",
      "4wks:\n",
      "\n",
      "real:\n",
      "[[ 1.514   1.9077 -1.4783]\n",
      " [ 0.174   1.7917 -1.9773]\n",
      " [-0.051   1.5367 -3.3643]\n",
      " [-0.589   3.1577 -1.6003]]\n",
      "fake:\n",
      "[[ 1.514   1.9077 -1.4783]\n",
      " [ 0.174   1.7917 -1.9773]\n",
      " [-0.051   1.5367 -3.3643]\n",
      " [-0.589   3.1577 -1.6003]]\n",
      "fake:\n",
      "[[ 1.  1.  1.]\n",
      " [ 1.  1.  1.]\n",
      " [ 1.  1.  1.]]\n",
      "-0.912921\n",
      "3.22969\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAW8AAAD8CAYAAAC4uSVNAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAACzdJREFUeJzt3W+Irnldx/HP13PWFHfDaEcT1+kkhSLin5gsUgIXlN02\nsihIqe1BxiFIUEhqexJIBPsoelLEwSShTIRcCFeNlRQRdfUc27VdXcPkRC7BcS3RhTBXvz04t3Q6\nnTNzzdxzz/qdeb1gOPfMfc09v9/v/Hhz7TX3dba6OwDM8pQnewAA7J94Awwk3gADiTfAQOINMJB4\nAwwk3gADiTfAQOINMNDpTbzozTff3GfOnNnESwMcSxcuXHisu7eWHr+ReJ85cybnz5/fxEsDHEtV\n9a/7Od5lE4CBxBtgIPEGGEi8AQYSb4CBFr3bpKouJvlGkm8neaK7dzY5KAB2t5+3Cr66ux/b2EgA\nWMxlE4CBlsa7k3yoqi5U1dlNDgiAvS29bPKq7n60qp6V5L6qeqS7P3rlAauon02S7e3tQx4mcNjO\n3HXvvo6/ePcdGxoJB7HozLu7H139eSnJPUlecY1jznX3TnfvbG0tvj0fgAPYM95V9Yyquum7j5O8\nNslDmx4YANe35LLJs5PcU1XfPf5d3f3BjY4KgF3tGe/u/lKSlx7BWABYyFsFAQYSb4CBxBtgIPEG\nGEi8AQYSb4CBxBtgIPEGGEi8AQYSb4CBxBtgIPEGGEi8AQYSb4CBxBtgIPEGGEi8AQYSb4CBxBtg\nIPEGGEi8AQYSb4CBxBtgIPEGGEi8AQYSb4CBxBtgIPEGGEi8AQYSb4CBxBtgIPEGGGhxvKvqVFX9\nY1W9b5MDAmBv+znzfnOSz29qIAAstyjeVXVLkjuSvH2zwwFgiaVn3n+S5HeTfGeDYwFgoT3jXVU/\nl+RSd1/Y47izVXW+qs5/5StfObQBAvD/LTnzfmWSn6+qi0neneTWqvqrqw/q7nPdvdPdO1tbW4c8\nTACutGe8u/v3u/uW7j6T5PVJ/qG7f23jIwPgurzPG2Cg0/s5uLs/kuQjGxkJAIs58wYYSLwBBhJv\ngIHEG2Ag8QYYSLwBBhJvgIHEG2Ag8QYYSLwBBhJvgIHEG2Ag8QYYSLwBBhJvgIHEG2Ag8QYYSLwB\nBhJvgIHEG2Ag8QYYSLwBBhJvgIHEG2Ag8QYYSLwBBhJvgIHEG2Ag8QYYSLwBBhJvgIHEG2Ag8QYY\naM94V9XTqupTVfVgVT1cVW87ioEBcH2nFxzzzSS3dvfjVXVDko9V1Qe6+5MbHhsA17FnvLu7kzy+\n+vSG1UdvclAA7G7RNe+qOlVVDyS5lOS+7r5/s8MCYDdLLpuku7+d5GVV9cwk91TVi7v7oSuPqaqz\nSc4myfb29qEPlKNz5q5793X8xbvv2NBIDu44zAF2s693m3T315J8OMlt13juXHfvdPfO1tbWYY0P\ngGtY8m6TrdUZd6rq6Ulek+SRTQ8MgOtbctnkOUneWVWncjn27+nu9212WADsZsm7TT6b5OVHMBYA\nFnKHJcBA4g0wkHgDDCTeAAOJN8BA4g0wkHgDDCTeAAOJN8BA4g0wkHgDDCTeAAOJN8BA4g0wkHgD\nDCTeAAOJN8BA4g0wkHgDDCTeAAOJN8BA4g0wkHgDDCTeAAOJN8BA4g0wkHgDDCTeAAOJN8BA4g0w\nkHgDDCTeAAPtGe+qel5VfbiqPldVD1fVm49iYABc3+kFxzyR5He6+zNVdVOSC1V1X3d/bsNjA+A6\n9jzz7u5/7+7PrB5/I8nnkzx30wMD4Pr2dc27qs4keXmS+zcxGACWWXLZJElSVTcm+dskb+nur1/j\n+bNJzibJ9vb2oQ3wyXbmrnv3dfzFu+/Y0EiOl5O2rvudb7L/OR/kZ5w0m16jo9yni868q+qGXA73\nX3f3e691THef6+6d7t7Z2to6zDECcJUl7zapJH+R5PPd/cebHxIAe1ly5v3KJHcmubWqHlh9/OyG\nxwXALva85t3dH0tSRzAWABZyhyXAQOINMJB4Awwk3gADiTfAQOINMJB4Awwk3gADiTfAQOINMJB4\nAwwk3gADiTfAQOINMJB4Awwk3gADiTfAQOINMJB4Awwk3gADiTfAQOINMJB4Awwk3gADiTfAQOIN\nMJB4Awwk3gADiTfAQOINMJB4Awwk3gADiTfAQHvGu6reUVWXquqhoxgQAHtbcub9l0lu2/A4ANiH\nPePd3R9N8h9HMBYAFjp9WC9UVWeTnE2S7e3tA7/Ombvu3dfxF+++Y6Ovv18Hef1Nz2G/r38SbXpf\nHAffi2t0kvf2of3CsrvPdfdOd+9sbW0d1ssCcA3ebQIwkHgDDLTkrYJ/k+QTSV5QVV+uqjduflgA\n7GbPX1h29xuOYiAALOeyCcBA4g0wkHgDDCTeAAOJN8BA4g0wkHgDDCTeAAOJN8BA4g0wkHgDDCTe\nAAOJN8BA4g0wkHgDDCTeAAOJN8BA4g0wkHgDDCTeAAOJN8BA4g0wkHgDDCTeAAOJN8BA4g0wkHgD\nDCTeAAOJN8BA4g0wkHgDDCTeAAMtindV3VZVX6iqL1bVXZseFAC72zPeVXUqyZ8muT3Ji5K8oape\ntOmBAXB9S868X5Hki939pe7+7yTvTvK6zQ4LgN0sifdzk/zbFZ9/efU1AJ4k1d27H1D1y0lu6+7f\nXH1+Z5Kf7O43XXXc2SRnV5++IMkXrnj65iSPHdagh7IG1uCkzz+xBsn11+CHu3tr6YucXnDMo0me\nd8Xnt6y+9n9097kk5671AlV1vrt3lg7qOLIG1uCkzz+xBsnhrcGSyyafTvJjVfUjVfXUJK9P8nfr\n/mAADm7PM+/ufqKq3pTk75OcSvKO7n544yMD4LqWXDZJd78/yfvX+DnXvJxywlgDa3DS559Yg+SQ\n1mDPX1gC8L3H7fEAA60V76p6WlV9qqoerKqHq+pt1zjmhVX1iar6ZlW99arnRt92fwjzv1hV/1RV\nD1TV+aMb+eFZuAa/WlWfXc3141X10iueG70HkkNZg5OyD163WoMHqup8Vb3qiudG74NDmP/+90B3\nH/gjSSW5cfX4hiT3J/mpq455VpKfSPJHSd56xddPJfmXJM9P8tQkDyZ50TrjOeqPdea/eu5ikpuf\n7HkcwRr8dJIfWD2+Pcn9x2UPrLsGJ2wf3Jj/vVT7kiSPHJd9sM78D7oH1jrz7sseX316w+qjrzrm\nUnd/Osm3rvr28bfdrzn/Y2HhGny8u/9z9eknc/legeQY7IFk7TU4FhauweO9KlWSZ1zx/Ph9sOb8\nD2Tta95VdaqqHkhyKcl93X3/wm89FrfdrzH/5PJf3oeq6sLqDtWR9rkGb0zygdXjY7EHkrXWIDlB\n+6CqfrGqHklyb5LfWH35WOyDNeafHGAPrB3v7v52d78sl88kXlFVL173NSdZc/6vWn3v7Ul+u6p+\nZiOD3LCla1BVr87lcP3eUY7vKKy5BidmH3T3Pd39wiS/kOQPj3qMm7Tm/Pe9Bw7t3Sbd/bUkH05y\n28JvWXTb/RQHmH+6+9HVn5eS3JPL//k41m5rUFUvSfL2JK/r7q+uvnys9kByoDU4UfvgimM+muT5\nVXVzjtk+OMD8D7QH1n23yVZVPXP1+OlJXpPkkYXfPv62+3XmX1XPqKqbvvs4yWuTPLSpsW7KkjWo\nqu0k701yZ3f/8xVPjd8DyXprcML2wY9WVa0e/3iS70vy1RyDfbDO/A+6BxbdYbmL5yR5Z13+HzY8\nJcl7uvt9VfVbSdLdf15VP5TkfJLvT/KdqnpLLv8m+es1/7b7A88/l/9lsXtWf5enk7yruz/4ZExi\nTXuuQZI/SPKDSf5sNd8nununj88/vXDgNUjy7JycffBLSX69qr6V5L+S/MrqF3jHYR8ceP5VdaA9\n4A5LgIHcYQkwkHgDDCTeAAOJN8BA4g0wkHgDDCTeAAOJN8BA/wPT2zPTLM3LXwAAAABJRU5ErkJg\ngg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4a687e0f90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/home/evan/Documents/deep_docking/datasets/v2015/4a9n/4a9n_ligand.mol2\n",
      "[[ 1.514   1.9077 -1.4783]\n",
      " [ 0.174   1.7917 -1.9773]\n",
      " [-0.051   1.5367 -3.3643]\n",
      " [-0.589   3.1577 -1.6003]]\n",
      "Butane:\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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NVe1LciawF/jzqnpy/B0PV8/nvwL4b+ASpm9kfBy4uoXnD5DkPcA3uotMPglQVX83q2Yt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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4a17c05790>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0415840148926\n",
      "Training epoch 55000\n",
      "Iter: 55000\n",
      "D loss: 9.076\n",
      "G_loss: 480.9\n",
      "4wks:\n",
      "\n",
      "real:\n",
      "[[ 1.514   1.9077 -1.4783]\n",
      " [ 0.174   1.7917 -1.9773]\n",
      " [-0.051   1.5367 -3.3643]\n",
      " [-0.589   3.1577 -1.6003]]\n",
      "fake:\n",
      "[[ 1.514   1.9077 -1.4783]\n",
      " [ 0.174   1.7917 -1.9773]\n",
      " [-0.051   1.5367 -3.3643]\n",
      " [-0.589   3.1577 -1.6003]]\n",
      "fake:\n",
      "[[ 1.  1.  1.]\n",
      " [ 1.  1.  1.]\n",
      " [ 1.  1.  1.]]\n",
      "-0.912921\n",
      "2.99816\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4a178450d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/home/evan/Documents/deep_docking/datasets/v2015/4a9n/4a9n_ligand.mol2\n",
      "[[ 1.514   1.9077 -1.4783]\n",
      " [ 0.174   1.7917 -1.9773]\n",
      " [-0.051   1.5367 -3.3643]\n",
      " [-0.589   3.1577 -1.6003]]\n",
      "Butane:\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4a6f9e1f10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0400221347809\n",
      "Training epoch 56000\n",
      "Iter: 56000\n",
      "D loss: 19.84\n",
      "G_loss: 466.0\n",
      "4wks:\n",
      "\n",
      "real:\n",
      "[[ 1.514   1.9077 -1.4783]\n",
      " [ 0.174   1.7917 -1.9773]\n",
      " [-0.051   1.5367 -3.3643]\n",
      " [-0.589   3.1577 -1.6003]]\n",
      "fake:\n",
      "[[ 1.514   1.9077 -1.4783]\n",
      " [ 0.174   1.7917 -1.9773]\n",
      " [-0.051   1.5367 -3.3643]\n",
      " [-0.589   3.1577 -1.6003]]\n",
      "fake:\n",
      "[[ 1.  1.  1.]\n",
      " [ 1.  1.  1.]\n",
      " [ 1.  1.  1.]]\n",
      "-0.912921\n",
      "2.57393\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4a0ff1f850>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/home/evan/Documents/deep_docking/datasets/v2015/4a9n/4a9n_ligand.mol2\n",
      "[[ 1.514   1.9077 -1.4783]\n",
      " [ 0.174   1.7917 -1.9773]\n",
      " [-0.051   1.5367 -3.3643]\n",
      " [-0.589   3.1577 -1.6003]]\n",
      "Butane:\n",
      "\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4a0fbf34d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0408918857574\n",
      "Training epoch 57000\n",
      "Iter: 57000\n",
      "D loss: 13.72\n",
      "G_loss: 491.6\n",
      "4wks:\n",
      "\n",
      "real:\n",
      "[[ 1.514   1.9077 -1.4783]\n",
      " [ 0.174   1.7917 -1.9773]\n",
      " [-0.051   1.5367 -3.3643]\n",
      " [-0.589   3.1577 -1.6003]]\n",
      "fake:\n",
      "[[ 1.514   1.9077 -1.4783]\n",
      " [ 0.174   1.7917 -1.9773]\n",
      " [-0.051   1.5367 -3.3643]\n",
      " [-0.589   3.1577 -1.6003]]\n",
      "fake:\n",
      "[[ 1.  1.  1.]\n",
      " [ 1.  1.  1.]\n",
      " [ 1.  1.  1.]]\n",
      "-0.912921\n",
      "-1.25096\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4a0e13f590>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/home/evan/Documents/deep_docking/datasets/v2015/4a9n/4a9n_ligand.mol2\n",
      "[[ 1.514   1.9077 -1.4783]\n",
      " [ 0.174   1.7917 -1.9773]\n",
      " [-0.051   1.5367 -3.3643]\n",
      " [-0.589   3.1577 -1.6003]]\n",
      "Butane:\n",
      "\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4a0ff68e10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0775928497314\n",
      "Training epoch 58000\n",
      "Iter: 58000\n",
      "D loss: 14.13\n",
      "G_loss: 493.6\n",
      "4wks:\n",
      "\n",
      "real:\n",
      "[[ 1.514   1.9077 -1.4783]\n",
      " [ 0.174   1.7917 -1.9773]\n",
      " [-0.051   1.5367 -3.3643]\n",
      " [-0.589   3.1577 -1.6003]]\n",
      "fake:\n",
      "[[ 1.514   1.9077 -1.4783]\n",
      " [ 0.174   1.7917 -1.9773]\n",
      " [-0.051   1.5367 -3.3643]\n",
      " [-0.589   3.1577 -1.6003]]\n",
      "fake:\n",
      "[[ 1.  1.  1.]\n",
      " [ 1.  1.  1.]\n",
      " [ 1.  1.  1.]]\n",
      "-0.912921\n",
      "-1.27497\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4a708fa350>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/home/evan/Documents/deep_docking/datasets/v2015/4a9n/4a9n_ligand.mol2\n",
      "[[ 1.514   1.9077 -1.4783]\n",
      " [ 0.174   1.7917 -1.9773]\n",
      " [-0.051   1.5367 -3.3643]\n",
      " [-0.589   3.1577 -1.6003]]\n",
      "Butane:\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4a6fe28ed0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-2122-89bf9846bab7>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     17\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     18\u001b[0m         _, D_loss_curr = sess.run(\n\u001b[0;32m---> 19\u001b[0;31m             [D_solver, D_loss] + d_ops, feed_dict=feed_dict)[0:2]\n\u001b[0m\u001b[1;32m     20\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     21\u001b[0m         \u001b[0m_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mG_loss_curr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msess\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mG_solver\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mG_loss\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mg_ops\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfeed_dict\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfeed_dict\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/home/evan/anaconda2/lib/python2.7/site-packages/tensorflow/python/client/session.pyc\u001b[0m in \u001b[0;36mrun\u001b[0;34m(self, fetches, feed_dict, options, run_metadata)\u001b[0m\n\u001b[1;32m    787\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    788\u001b[0m       result = self._run(None, fetches, feed_dict, options_ptr,\n\u001b[0;32m--> 789\u001b[0;31m                          run_metadata_ptr)\n\u001b[0m\u001b[1;32m    790\u001b[0m       \u001b[0;32mif\u001b[0m \u001b[0mrun_metadata\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    791\u001b[0m         \u001b[0mproto_data\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtf_session\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTF_GetBuffer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrun_metadata_ptr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/home/evan/anaconda2/lib/python2.7/site-packages/tensorflow/python/client/session.pyc\u001b[0m in \u001b[0;36m_run\u001b[0;34m(self, handle, fetches, feed_dict, options, run_metadata)\u001b[0m\n\u001b[1;32m    995\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mfinal_fetches\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mfinal_targets\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    996\u001b[0m       results = self._do_run(handle, final_targets, final_fetches,\n\u001b[0;32m--> 997\u001b[0;31m                              feed_dict_string, options, run_metadata)\n\u001b[0m\u001b[1;32m    998\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    999\u001b[0m       \u001b[0mresults\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/home/evan/anaconda2/lib/python2.7/site-packages/tensorflow/python/client/session.pyc\u001b[0m in \u001b[0;36m_do_run\u001b[0;34m(self, handle, target_list, fetch_list, feed_dict, options, run_metadata)\u001b[0m\n\u001b[1;32m   1130\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mhandle\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1131\u001b[0m       return self._do_call(_run_fn, self._session, feed_dict, fetch_list,\n\u001b[0;32m-> 1132\u001b[0;31m                            target_list, options, run_metadata)\n\u001b[0m\u001b[1;32m   1133\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1134\u001b[0m       return self._do_call(_prun_fn, self._session, handle, feed_dict,\n",
      "\u001b[0;32m/home/evan/anaconda2/lib/python2.7/site-packages/tensorflow/python/client/session.pyc\u001b[0m in \u001b[0;36m_do_call\u001b[0;34m(self, fn, *args)\u001b[0m\n\u001b[1;32m   1137\u001b[0m   \u001b[0;32mdef\u001b[0m \u001b[0m_do_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1138\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1139\u001b[0;31m       \u001b[0;32mreturn\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1140\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mOpError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1141\u001b[0m       \u001b[0mmessage\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcompat\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mas_text\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmessage\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/home/evan/anaconda2/lib/python2.7/site-packages/tensorflow/python/client/session.pyc\u001b[0m in \u001b[0;36m_run_fn\u001b[0;34m(session, feed_dict, fetch_list, target_list, options, run_metadata)\u001b[0m\n\u001b[1;32m   1119\u001b[0m         return tf_session.TF_Run(session, options,\n\u001b[1;32m   1120\u001b[0m                                  \u001b[0mfeed_dict\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfetch_list\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtarget_list\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1121\u001b[0;31m                                  status, run_metadata)\n\u001b[0m\u001b[1;32m   1122\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1123\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_prun_fn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msession\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhandle\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfeed_dict\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfetch_list\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "every = 1000\n",
    "#train_dataset = features[:1]*32\n",
    "train_dataset = features[:96*2]\n",
    "n_train = len(train_dataset)\n",
    "\n",
    "for it in range(1000000):\n",
    "    a = time.time()\n",
    "    random.shuffle(train_dataset)\n",
    "    \n",
    "    batch_sched = list(range(0, n_train+1,S))\n",
    "    for j in range(0, len(batch_sched)-1):\n",
    "        start = batch_sched[j]\n",
    "        stop = batch_sched[j+1]\n",
    "        \n",
    "\n",
    "        feed_dict = construct_feed_dict(train_dataset, start, stop)\n",
    "\n",
    "        _, D_loss_curr = sess.run(\n",
    "            [D_solver, D_loss] + d_ops, feed_dict=feed_dict)[0:2]\n",
    "        \n",
    "        _, G_loss_curr = sess.run([G_solver, G_loss] + g_ops, feed_dict=feed_dict)[0:2]\n",
    "        \n",
    "        d_losses.append(D_loss_curr)\n",
    "        g_losses.append(G_loss_curr)\n",
    "    \n",
    "    if it % every == 0:\n",
    "        \n",
    "        print(time.time()-a)\n",
    "        print(\"Training epoch %d\" %it)\n",
    "                \n",
    "        print('Iter: {}'.format(it))\n",
    "        print('D loss: {:.4}'. format(D_loss_curr))\n",
    "        print('G_loss: {:.4}'.format(G_loss_curr))\n",
    "        feed_dict = construct_feed_dict(features[:1]*S, 0, S, shuffle_inds=False, keep_prob_val=1., train=False)\n",
    "        \n",
    "        print(\"4wks:\\n\")\n",
    "        \n",
    "        print(\"real:\")\n",
    "        print(feed_dict[mol_xyz][0][:4])\n",
    "        print(\"fake:\")\n",
    "        print(sess.run(G_sample, feed_dict=feed_dict)[0][:4])\n",
    "        print(\"fake:\")\n",
    "        print(sess.run(rot_mat_i, feed_dict=feed_dict))\n",
    "\n",
    "        print(np.mean(sess.run(dihed_tensor, feed_dict=feed_dict)[:, 0]))\n",
    "        print(np.mean(sess.run(dihed, feed_dict=feed_dict)[:, 0]))\n",
    "        plt.hist(sess.run(dihed, feed_dict=feed_dict)[:, 0], bins=25)\n",
    "        plt.show()\n",
    "        \n",
    "        \n",
    "        generate_molecule(features[0][0], sess.run(G_sample, feed_dict=feed_dict)[0], \"/home/evan/Documents/deep_docking/dcgan_pg/DCGAN-tensorflow/generated_mols/%s_test%d.mol\" %(features[0][0].split(\".\")[0].split(\"/\")[-1], it))\n",
    "        \n",
    "        print(\"Butane:\\n\")\n",
    "        feed_dict = construct_feed_dict(features[len(features)-32:], 0, S, shuffle_inds=False, keep_prob_val=1., train=False)\n",
    "        plt.hist(sess.run(dihed, feed_dict=feed_dict)[:, 0], bins=25)\n",
    "        plt.show()\n",
    "        #generate_molecule(features[1][0], sess.run(G_sample, feed_dict=feed_dict)[1], \"/home/evan/Documents/deep_docking/dcgan_pg/DCGAN-tensorflow/generated_mols/%s_test%d.mol\" %(features[1][0].split(\".\")[0].split(\"/\")[-1], it))\n",
    "\n",
    "        #feed_dict = construct_feed_dict(features, len(features)-S, len(features), shuffle_inds=False, keep_prob_val=1.)\n",
    "        #generate_molecule(features[-1][0], sess.run(G_sample, feed_dict=feed_dict)[0], \"/home/evan/Documents/deep_docking/dcgan_pg/DCGAN-tensorflow/generated_mols/%s_test%d.mol\" %(features[-1][0].split(\".\")[0].split(\"/\")[-1], it))\n",
    "\n",
    "        \n",
    "        \n",
    "\n",
    "        #print(\"output_i\")\n",
    "        #print(sess.run(G_sample_output_i, feed_dict=feed_dict))\n",
    "        #print(sess.run(G_sample, feed_dict=feed_dict))\n",
    "\n",
    "    #random.shuffle(train_dataset)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "features[4]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "every = 1000\n",
    "#train_dataset = features[:96]\n",
    "train_dataset = features[:1]*S\n",
    "n_train = len(train_dataset)\n",
    "\n",
    "for it in range(1000000):\n",
    "    a = time.time()\n",
    "    random.shuffle(train_dataset)\n",
    "    \n",
    "    batch_sched = list(range(0, n_train+1,S))\n",
    "    for j in range(0, len(batch_sched)-1):\n",
    "        start = batch_sched[j]\n",
    "        stop = batch_sched[j+1]\n",
    "        \n",
    "        for _ in range(5):\n",
    "            feed_dict = construct_feed_dict(train_dataset, start, stop)\n",
    "\n",
    "            _, D_loss_curr, _ = sess.run(\n",
    "                [D_solver, D_loss, clip_D], feed_dict=feed_dict)\n",
    "        \n",
    "        feed_dict = construct_feed_dict(train_dataset, start, stop)\n",
    "\n",
    "        _, G_loss_curr = sess.run([G_solver, G_loss], feed_dict=feed_dict)\n",
    "        \n",
    "        d_losses.append(D_loss_curr)\n",
    "        g_losses.append(G_loss_curr)\n",
    "    \n",
    "    if it % every == 0:\n",
    "        \n",
    "        print(time.time()-a)\n",
    "        print(\"Training epoch %d\" %it)\n",
    "                \n",
    "        print('Iter: {}'.format(it))\n",
    "        print('D loss: {:.4}'. format(D_loss_curr))\n",
    "        print('G_loss: {:.4}'.format(G_loss_curr))\n",
    "        feed_dict = construct_feed_dict(features[:1]*S, 0, S, shuffle_inds=False, keep_prob_val=1.)\n",
    "        \n",
    "        print(\"4wks:\\n\")\n",
    "        \n",
    "        print(\"real:\")\n",
    "        print(feed_dict[mol_xyz][0][:4])\n",
    "        print(\"fake:\")\n",
    "        print(sess.run(G_sample, feed_dict=feed_dict)[0][:4])\n",
    "        print(\"fake:\")\n",
    "        print(sess.run(rot_mat_i, feed_dict=feed_dict))\n",
    "\n",
    "        print(np.mean(sess.run(dihed, feed_dict=feed_dict)[:, 0]))\n",
    "        plt.hist(sess.run(dihed, feed_dict=feed_dict)[:, 0])\n",
    "        plt.show()\n",
    "        \n",
    "        \n",
    "        generate_molecule(features[0][0], sess.run(G_sample, feed_dict=feed_dict)[0], \"/home/evan/Documents/deep_docking/dcgan_pg/DCGAN-tensorflow/generated_mols/%s_test%d.mol\" %(features[0][0].split(\".\")[0].split(\"/\")[-1], it))\n",
    "        \n",
    "        print(\"Butane:\\n\")\n",
    "        feed_dict = construct_feed_dict(features[len(features)-32:], 0, S, shuffle_inds=False, keep_prob_val=1.)\n",
    "        plt.hist(sess.run(dihed, feed_dict=feed_dict)[:, 0])\n",
    "        plt.show()\n",
    "        #generate_molecule(features[1][0], sess.run(G_sample, feed_dict=feed_dict)[1], \"/home/evan/Documents/deep_docking/dcgan_pg/DCGAN-tensorflow/generated_mols/%s_test%d.mol\" %(features[1][0].split(\".\")[0].split(\"/\")[-1], it))\n",
    "\n",
    "        #feed_dict = construct_feed_dict(features, len(features)-S, len(features), shuffle_inds=False, keep_prob_val=1.)\n",
    "        #generate_molecule(features[-1][0], sess.run(G_sample, feed_dict=feed_dict)[0], \"/home/evan/Documents/deep_docking/dcgan_pg/DCGAN-tensorflow/generated_mols/%s_test%d.mol\" %(features[-1][0].split(\".\")[0].split(\"/\")[-1], it))\n",
    "\n",
    "        \n",
    "        \n",
    "\n",
    "        #print(\"output_i\")\n",
    "        #print(sess.run(G_sample_output_i, feed_dict=feed_dict))\n",
    "        #print(sess.run(G_sample, feed_dict=feed_dict))\n",
    "\n",
    "    #random.shuffle(train_dataset)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 321,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7feedffcfd50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "S=16\n",
    "feed_dict = construct_feed_dict(features, len(features)-S, len(features), shuffle_inds=False)\n",
    "\n",
    "plt.hist(np.squeeze(sess.run(dihed, feed_dict=feed_dict)), bins=25)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 216,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('/home/evan/Documents/deep_docking/datasets/v2015/2ohk/2ohk_ligand.mol2',\n",
       " array([[-1.2731,  0.9987, -2.2001],\n",
       "        [-0.5031,  1.0397, -1.0801],\n",
       "        [-0.0411,  2.2447, -0.6841],\n",
       "        [ 0.7269,  2.2857,  0.4109],\n",
       "        [ 1.0309,  1.1747,  1.1729],\n",
       "        [ 0.5309, -0.0663,  0.8089],\n",
       "        [ 0.7909, -1.2213,  1.5379],\n",
       "        [ 0.2719, -2.4343,  1.1299],\n",
       "        [-0.4991, -2.5073, -0.0101],\n",
       "        [-0.7681, -1.3733, -0.7401],\n",
       "        [-0.2671, -0.1413, -0.3461],\n",
       "        [ 0.    ,  0.    ,  0.    ]]),\n",
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       " array([[ 0.]]),\n",
       " array([[[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
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       " array([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], dtype=uint8),\n",
       " array([[ 0.,  0.,  0.]]),\n",
       " (array([[1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
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       "          0, 0, 0, 0, 1, 0, 0, 0, 1],\n",
       "         [1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
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       "          0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0,\n",
       "          0, 0, 0, 0, 1, 0, 0, 0, 1],\n",
       "         [1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
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       "         [1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "          0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
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       "          0, 0, 0, 0, 1, 0, 0, 0, 1],\n",
       "         [1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "          0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "          0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0,\n",
       "          0, 0, 0, 0, 1, 0, 0, 0, 1],\n",
       "         [1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "          0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "          0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0,\n",
       "          0, 0, 0, 0, 1, 0, 0, 0, 1],\n",
       "         [1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "          0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "          0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0,\n",
       "          0, 0, 0, 0, 1, 0, 0, 0, 1],\n",
       "         [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "          0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "          0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "          0, 0, 0, 0, 0, 0, 0, 0, 0]], dtype=uint8)))"
      ]
     },
     "execution_count": 216,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "feed_dict = construct_feed_dict(features, len(features)-S, len(features), shuffle_inds=False)\n",
    "generate_molecule(features[-1][0], sess.run(G_sample, feed_dict=feed_dict)[0], \"/home/evan/Documents/deep_docking/dcgan_pg/DCGAN-tensorflow/generated_mols/%s_test%d.mol\" %(features[-1][0].split(\".\")[0].split(\"/\")[-1], it))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "np.min(results[0])\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "np.where(features[0][1][:,:,:,:-1] == 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "\"\"\"\n",
    "def discriminator(W_list, b_list, h_list, L_list, n_layers, mols):\n",
    "    mols = tf.add(mols, mol_noise)\n",
    "    with tf.device('/gpu:0'):\n",
    "        for layer_idx in range(n_layers):\n",
    "            h_list.append(tf.nn.dropout(adjacency_conv_layer(h_list[layer_idx], W_list[layer_idx],\n",
    "                                             b_list[layer_idx], adj_list[layer_idx], \n",
    "                                             clades_list[layer_idx],\n",
    "                                             L_list[layer_idx], L_list[layer_idx+1],\n",
    "                                             layer_idx, S, B_list[layer_idx]), keep_prob))\n",
    "        h_final = h_list[-1]\n",
    "        dist_matrix = tf.concat([tf.reshape(compute_distance_matrix(tf.reshape(mol, [B, 3])), (1, B, B)) for mol in tf.split(mols, S, axis=0)], axis=0)\n",
    "        print(\"dist_matrix\")\n",
    "        print(dist_matrix)\n",
    "        #dist_matrix = compute_distance_matrix(mol)\n",
    "        bond_dist_gen = tf.multiply(dist_matrix, adj_matrix)\n",
    "        wrong_bond_loss = tf.divide(tf.reshape(tf.multiply(tf.reduce_sum(tf.square(tf.subtract(bond_dist_mat, bond_dist_gen)), axis=[1,2]), lam1), (S,1)), n_bonds)\n",
    "        print(\"wrong_bond_loss\")\n",
    "        print(wrong_bond_loss)\n",
    "\n",
    "        angles = []\n",
    "        for i in range(0, B):\n",
    "            for j in range(0, B):\n",
    "                ij = mols[:, i,:] - mols[:,j,:]\n",
    "                ij = ij / tf.sqrt(tf.reduce_sum(tf.square(ij), 1, keep_dims=True))\n",
    "                #ij shape should be (S, 3)\n",
    "               # print(\"ij\")\n",
    "                #print(ij)\n",
    "                for k in range(0,B):\n",
    "                    ik = mols[:,i,:] - mols[:,k,:]\n",
    "                    ik = ik / tf.sqrt(tf.reduce_sum(tf.square(ik), 1, keep_dims=True))\n",
    "\n",
    "                    #ik shape should be (S, 3)\n",
    "                    #if i == 0 and j == 0 and k == 0: \n",
    "                    #   print(\"ik\")\n",
    "                     #   print(ik)\n",
    "                        \n",
    "                    dp = tf.reduce_sum(tf.multiply(ij, ik), axis=1, keep_dims=True)\n",
    "                    #dp shape should be (S,1)\n",
    "                    #if i ==0 and j ==0 and k==0:\n",
    "                    #    print(\"dp\")\n",
    "                    #    print(dp)\n",
    "                    if i == j or i == k or j == k:\n",
    "                        angle = tf.Variable(tf.constant(np.zeros([4,1]).astype(np.float32)))\n",
    "                    else:\n",
    "                        angle = tf.acos(tf.clip_by_value(tf.reduce_sum(tf.multiply(ij, ik), axis=1, keep_dims=True), -1.0, 1.0))\n",
    "                    #if i ==0 and j==0 and k==0:\n",
    "                        #print(\"angle\")\n",
    "                        #print(angle)\n",
    "                    angles.append(angle)\n",
    "        angles_gen = tf.reshape(tf.concat(angles, axis=1), [S, B, B, B])\n",
    "        angles_gen = tf.multiply(angles_gen, angle_tuples)\n",
    "        wrong_angles_loss = tf.divide(tf.reshape(tf.multiply(tf.reduce_sum(tf.square(tf.subtract(angle_tensor, angles_gen)), axis=[1,2,3]), lam2), (S,1)), n_angles)\n",
    "                \n",
    "        feat_i = tf.reshape(h_final, (S, B, 1, L_list[n_layers]))\n",
    "        feat_i = tf.tile(feat_i, [1, 1, B, 1])\n",
    "        \n",
    "        feat_j = tf.reshape(h_final, (S, B, 1, L_list[n_layers]))\n",
    "        feat_j = tf.transpose(feat_j, perm=[0, 2, 1, 3])\n",
    "        feat_j = tf.tile(feat_j, [1, B, 1, 1])\n",
    "        \n",
    "        d_h1 = tf.concat([tf.reshape(dist_matrix, [S, B, B, 1]), feat_i, feat_j], axis=3)\n",
    "        d_h1 = tf.reshape(d_h1, [S, B, B*(L_list[n_layers]*2+1)])\n",
    "        d_h2 = tf.nn.tanh(tf.matmul(d_h1, d_w1) + d_b1)\n",
    "        d_h2 = tf.divide(tf.reduce_sum(tf.matmul(d_h2, d_w2) + d_b2, axis=1), n_bonds)#, axis=1\n",
    "        print(\"d_h2\")\n",
    "        print(d_h2)\n",
    "        \n",
    "    \n",
    "    return(wrong_bond_loss + wrong_angles_loss + d_h2, bond_dist_gen, angles_gen, wrong_bond_loss, wrong_angles_loss)\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "mol = Chem.MolFromMol2File(ligand_files[0])\n",
    "c = mol.GetConformer(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "c.SetAtomPosition(0, [0., 0., 0.])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "np.where(features[2][1][:,:,:,:-1] == 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "results = sess.run(G_sample, feed_dict=feed_dict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "sess.run(pred_real, feed_dict=feed_dict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "np.max(results[0][:,:,:,:], axis=-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "features[1][0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "mol = Chem.MolFromMol2File(features[1][0])\n",
    "mol.GetNumAtoms()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from rdkit.Chem import AllChem\n",
    "AllChem.Compute2DCoords(mol)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from rdkit.Chem import Draw\n",
    "\n",
    "Draw.MolToImage(mol)"
   ]
  }
 ],
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