paccmann_chemistry

Generative models of chemical data for PaccMann^RL. For example, a SMILES/SELFIES VAE using stack-augmented GRUs in both encoder and decoder. For details, see for example:

• PaccMann^RL: De novo generation of hit-like anticancer molecules from transcriptomic data via reinforcement learning (iScience, 2021).

• Data-driven molecular design for discovery and synthesis of novel ligands: a case study on SARS-CoV-2 (Machine Learning: Science and Technology, 2021).

Requirements

• conda>=3.7

Installation

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.yml

Activate the environment:

conda activate paccmann_chemistry

Install in editable mode for development:

pip install -e .

Example usage

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 exit

params_filepath could point to examples/example_params.json, examples for other files can be downloaded from here.

References

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}
}