diff --git a/deepchem/models/torch_models/__init__.py b/deepchem/models/torch_models/__init__.py index 6ec995428c..e104f380be 100644 --- a/deepchem/models/torch_models/__init__.py +++ b/deepchem/models/torch_models/__init__.py @@ -33,6 +33,7 @@ from deepchem.models.torch_models.flows import Flow, Affine, MaskedAffineFlow, ActNorm, ClampExp, ConstScaleLayer, MLPFlow, NormalizingFlow, NormalizingFlowModel from deepchem.models.torch_models.unet import UNet, UNetModel from deepchem.models.torch_models.graphconvmodel import _GraphConvTorchModel +from deepchem.models.torch_models.smiles2vec import Smiles2Vec try: from deepchem.models.torch_models.dmpnn import DMPNN, DMPNNModel from deepchem.models.torch_models.gnn import GNN, GNNHead, GNNModular diff --git a/deepchem/models/torch_models/smiles2vec.py b/deepchem/models/torch_models/smiles2vec.py new file mode 100644 index 0000000000..7409fcbd31 --- /dev/null +++ b/deepchem/models/torch_models/smiles2vec.py @@ -0,0 +1,179 @@ +import torch.nn as nn +import math +from typing import List, Dict, Optional + + +class Smiles2Vec(nn.Module): + """ + Implements the Smiles2Vec model, that learns neural representations of SMILES + strings which can be used for downstream tasks. + + The goal here is to take SMILES strings as inputs, turn them into vector representations + which can then be used in predicting molecular properties. + + The model consists of an Embedding layer that retrieves embeddings for each + character in the SMILES string. These embeddings are learnt jointly with the + rest of the model. The output from the embedding layer is a tensor of shape + (batch_size, seq_len, embedding_dim). This tensor can optionally be fed + through a 1D convolutional layer, before being passed to a series of RNN cells + (optionally bidirectional). The final output from the RNN cells aims + to have learnt the temporal dependencies in the SMILES string, and in turn + information about the structure of the molecule, which is then used for + molecular property prediction. + + In the paper, the authors also train an explanation mask to endow the model + with interpretability and gain insights into its decision making. This segment + is currently not a part of this implementation as this was + developed for the purpose of investigating a transfer learning protocol, + ChemNet. + + References + ---------- + .. [1] Goh et al., "SMILES2vec: An Interpretable General-Purpose Deep Neural Network for + Predicting Chemical Properties" (https://arxiv.org/pdf/1712.02034.pdf) + .. [2] Chemnet (https://arxiv.org/abs/1712.02734) + """ + + def __init__( + self, + char_to_idx: Dict, + n_tasks: int = 10, + max_seq_len: int = 270, + embedding_dim: int = 50, + n_classes: int = 2, + use_bidir: bool = True, + use_conv: bool = True, + filters: int = 192, + kernel_size: int = 3, + strides: int = 1, + rnn_sizes: List[int] = [224, 384], + rnn_types: List[str] = ["GRU", "GRU"], + mode: str = "regression", + ): + """ + Parameters + ---------- + char_to_idx: dict, + char_to_idx contains character to index mapping for SMILES characters + embedding_dim: int, default 50 + Size of character embeddings used. + use_bidir: bool, default True + Whether to use BiDirectional RNN Cells + use_conv: bool, default True + Whether to use a conv-layer + kernel_size: int, default 3 + Kernel size for convolutions + filters: int, default 192 + Number of filters + strides: int, default 1 + Strides used in convolution + rnn_sizes: list[int], default [224, 384] + Number of hidden units in the RNN cells + mode: str, default regression + Whether to use model for regression or classification + """ + + super(Smiles2Vec, self).__init__() + + self.char_to_idx = char_to_idx + self.n_classes = n_classes + self.max_seq_len = max_seq_len + self.embedding_dim = embedding_dim + self.use_bidir = use_bidir + self.use_conv = use_conv + if use_conv: + self.kernel_size = kernel_size + self.filters = filters + self.strides = strides + self.rnn_types = rnn_types + self.rnn_sizes = rnn_sizes + assert len(rnn_sizes) == len( + rnn_types), "Should have same number of hidden units as RNNs" + self.n_tasks = n_tasks + self.mode = mode + self.embedding = nn.Embedding(num_embeddings=len(self.char_to_idx), + embedding_dim=self.embedding_dim) + + if use_conv: + self.conv1d = nn.Conv1d(embedding_dim, + filters, + kernel_size, + stride=strides) + self.conv_output = math.floor( + (max_seq_len - kernel_size + 1) / strides) + + self.RNN_DICT = {'RNN': nn.RNN, 'LSTM': nn.LSTM, 'GRU': nn.GRU} + self.filters = filters + self.rnn_sizes = rnn_sizes + self.use_bidir = use_bidir + self.rnn_layers: nn.ModuleList = nn.ModuleList() + self.output_activation: Optional[nn.Module] = None + + # Define RNN layers + for idx, rnn_type in enumerate(self.rnn_types): + rnn_layer = self.RNN_DICT[rnn_type]( + input_size=self.filters if idx == 0 else self.rnn_sizes[idx - + 1], + hidden_size=self.rnn_sizes[idx], + batch_first=True, + bidirectional=self.use_bidir) + self.rnn_layers.append(rnn_layer) + + # Create the last RNN layer separately + last_layer_input_size = self.rnn_sizes[-2] * (2 + if self.use_bidir else 1) + last_rnn_layer = self.RNN_DICT[self.rnn_types[-1]] + self.last_rnn_layer = last_rnn_layer(last_layer_input_size, + self.rnn_sizes[-1], + batch_first=True, + bidirectional=self.use_bidir) + + input_size = self.rnn_sizes[-1] * (2 if self.use_bidir else 1) + + # Define the fully connected layer for final output + if self.mode == "classification": + self.fc = nn.Linear(input_size, self.n_tasks * self.n_classes) + else: + self.fc = nn.Linear(input_size, self.n_tasks) + + if self.mode == "classification": + if self.n_classes == 2: + self.output_activation = nn.Sigmoid() + else: + self.output_activation = nn.Softmax(dim=-1) + else: + self.output_activation = None + + def forward(self, smiles_seqs: List): + """Build the model.""" + rnn_input = self.embedding(smiles_seqs) + + if self.use_conv: + # Convert to (batch_size, seq_len, filters) for conv1d + rnn_input = rnn_input.permute(0, 2, 1) + rnn_input = self.conv1d(rnn_input) + rnn_input = rnn_input.permute( + 0, 2, 1) # Convert back to (batch_size, seq_len, filters) + + # RNN layers + rnn_embeddings = rnn_input + for layer in self.rnn_layers[:-1]: + rnn_embeddings, _ = layer(rnn_embeddings) + + # Pass through the last RNN layer + rnn_embeddings, _ = self.last_rnn_layer(rnn_embeddings) + + # Global Average Pooling + x = rnn_embeddings.mean(dim=1) + + Logits = self.fc(x) + + if self.mode == "classification": + Logits = Logits.view(-1, self.n_tasks, self.n_classes) + + if self.output_activation is not None: + output = self.output_activation(Logits) + return Logits, output + else: + Logits = Logits.view(-1, self.n_tasks, 1) + return Logits diff --git a/deepchem/models/torch_models/tests/assets/chembl_25_small.csv b/deepchem/models/torch_models/tests/assets/chembl_25_small.csv new file mode 100644 index 0000000000..b83e3dba25 --- /dev/null +++ b/deepchem/models/torch_models/tests/assets/chembl_25_small.csv @@ -0,0 +1,101 @@ 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diff --git a/deepchem/models/torch_models/tests/test_smiles2vec.py b/deepchem/models/torch_models/tests/test_smiles2vec.py new file mode 100644 index 0000000000..6f2bb6e9f6 --- /dev/null +++ b/deepchem/models/torch_models/tests/test_smiles2vec.py @@ -0,0 +1,85 @@ +import os +import numpy as np +import tempfile +import pytest + +import deepchem as dc +from deepchem.feat import create_char_to_idx, SmilesToSeq +from deepchem.molnet.load_function.chembl25_datasets import CHEMBL25_TASKS + +try: + import torch +except ModuleNotFoundError: + pass + + +def get_dataset(mode="regression", + featurizer="smiles2seq", + max_seq_len=20, + data_points=10, + n_tasks=5): + dataset_file = os.path.join(os.path.dirname(__file__), "assets", + "chembl_25_small.csv") + if featurizer == "smiles2seq": + max_len = 250 + pad_len = 10 + char_to_idx = create_char_to_idx(dataset_file, + max_len=max_len, + smiles_field="smiles") + + feat = SmilesToSeq(char_to_idx=char_to_idx, + max_len=max_len, + pad_len=pad_len) + + loader = dc.data.CSVLoader(tasks=CHEMBL25_TASKS, + feature_field='smiles', + featurizer=feat) + + dataset = loader.create_dataset(inputs=[dataset_file], + shard_size=10000, + data_dir=tempfile.mkdtemp()) + + w = np.ones(shape=(data_points, n_tasks)) + + if mode == 'classification': + y = np.random.randint(0, 2, size=(data_points, n_tasks)) + metric = dc.metrics.Metric(dc.metrics.roc_auc_score, + np.mean, + mode="classification") + else: + y = np.random.normal(size=(data_points, n_tasks)) + metric = dc.metrics.Metric(dc.metrics.mean_absolute_error, + mode="regression") + + if featurizer == "smiles2seq": + dataset = dc.data.NumpyDataset(dataset.X[:data_points, :max_seq_len], y, + w, dataset.ids[:data_points]) + else: + dataset = dc.data.NumpyDataset(dataset.X[:data_points], y, w, + dataset.ids[:data_points]) + + if featurizer == "smiles2seq": + return dataset, metric, char_to_idx + else: + return dataset, metric + + +@pytest.mark.torch +def test_smiles2vec_model(): + from deepchem.models.torch_models import Smiles2Vec + + n_tasks = 10 + max_seq_len = 20 + + _, _, char_to_idx = get_dataset( + mode="regression", + featurizer="smiles2seq", + n_tasks=n_tasks, + max_seq_len=max_seq_len, + ) + model = Smiles2Vec(char_to_idx) + input = torch.randint(low=0, high=len(char_to_idx), size=(1, max_seq_len)) + # Ex: input = torch.tensor([[32,32,32,32,32,32,25,29,15,17,29,29,32,32,32,32,32,32,32,32]]) + + logits = model.forward(input) + assert np.shape(logits) == (1, n_tasks, 1) diff --git a/docs/source/api_reference/models.rst b/docs/source/api_reference/models.rst index 6bb1e1e3ee..40e9d2734e 100644 --- a/docs/source/api_reference/models.rst +++ b/docs/source/api_reference/models.rst @@ -568,6 +568,12 @@ _GraphConvTorchModel .. autoclass:: deepchem.models.torch_models._GraphConvTorchModel :members: +Smiles2Vec +-------------------- + +.. autoclass:: deepchem.models.torch_models.Smiles2Vec + :members: + PyTorch Lightning Models ========================