Generative models of chemical data for PaccMannRL. For example, a SMILES/SELFIES VAE using stack-augmented GRUs in both encoder and decoder. For details, see for example:
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PaccMannRL: De novo generation of hit-like anticancer molecules from transcriptomic data via reinforcement learning (iScience, 2021).
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Data-driven molecular design for discovery and synthesis of novel ligands: a case study on SARS-CoV-2 (Machine Learning: Science and Technology, 2021).
conda>=3.7
The library itself has few dependencies (see setup.py) with loose requirements.
To run the example training script we provide environment files under examples/.
Create a conda environment:
conda env create -f examples/conda.ymlActivate the environment:
conda activate paccmann_chemistryInstall in editable mode for development:
pip install -e .In the examples directory is a training script train_vae.py that makes use of paccmann_chemistry.
(paccmann_chemistry) $ python examples/train_vae.py -h
usage: train_vae.py [-h]
train_smiles_filepath test_smiles_filepath
smiles_language_filepath model_path params_filepath
training_name
Chemistry VAE training script.
positional arguments:
train_smiles_filepath
Path to the train data file (.smi).
test_smiles_filepath Path to the test data file (.smi).
smiles_language_filepath
Path to SMILES language object.
model_path Directory where the model will be stored.
params_filepath Path to the parameter file.
training_name Name for the training.
optional arguments:
-h, --help show this help message and exitparams_filepath could point to examples/example_params.json, examples for other files can be downloaded from here.
If you use paccmann_chemistry in your projects, please cite the following:
@article{born2021datadriven,
author = {Born, Jannis and Manica, Matteo and Cadow, Joris and Markert, Greta and Mill, Nil Adell and Filipavicius, Modestas and Janakarajan, Nikita and Cardinale, Antonio and Laino, Teodoro and {Rodr{\'{i}}guez Mart{\'{i}}nez}, Mar{\'{i}}a},
doi = {10.1088/2632-2153/abe808},
issn = {2632-2153},
journal = {Machine Learning: Science and Technology},
number = {2},
pages = {025024},
title = {{Data-driven molecular design for discovery and synthesis of novel ligands: a case study on SARS-CoV-2}},
url = {https://iopscience.iop.org/article/10.1088/2632-2153/abe808},
volume = {2},
year = {2021}
}
@article{born2021paccmannrl,
title = {PaccMann\textsuperscript{RL}: De novo generation of hit-like anticancer molecules from transcriptomic data via reinforcement learning},
journal = {iScience},
volume = {24},
number = {4},
pages = {102269},
year = {2021},
issn = {2589-0042},
doi = {https://doi.org/10.1016/j.isci.2021.102269},
url = {https://www.cell.com/iscience/fulltext/S2589-0042(21)00237-6},
author = {Born, Jannis and Manica, Matteo and Oskooei, Ali and Cadow, Joris and Markert, Greta and {Rodr{\'{i}}guez Mart{\'{i}}nez}, Mar{\'{i}}a}
}