https://github.com/paccmann/paccmann_chemistry
Generative models of chemical data for PaccMann^RL
https://github.com/paccmann/paccmann_chemistry
deep-learning drug-discovery generative-model molecule-generation vae
Last synced: 2 months ago
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Generative models of chemical data for PaccMann^RL
- Host: GitHub
- URL: https://github.com/paccmann/paccmann_chemistry
- Owner: PaccMann
- License: mit
- Created: 2019-11-03T13:06:32.000Z (over 5 years ago)
- Default Branch: master
- Last Pushed: 2023-06-02T07:12:28.000Z (about 2 years ago)
- Last Synced: 2025-03-24T16:53:19.471Z (3 months ago)
- Topics: deep-learning, drug-discovery, generative-model, molecule-generation, vae
- Language: Python
- Homepage:
- Size: 123 KB
- Stars: 14
- Watchers: 5
- Forks: 8
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
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# paccmann_chemistry
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:
- [](https://doi.org/10.1016/j.isci.2021.102269) [PaccMannRL: De novo generation of hit-like anticancer molecules from transcriptomic data via reinforcement learning](https://www.cell.com/iscience/fulltext/S2589-0042(21)00237-6) (_iScience_, 2021).
- [](https://doi.org/10.1088/2632-2153/abe808) [Data-driven molecular design for discovery and synthesis of novel ligands: a case study on SARS-CoV-2](https://iopscience.iop.org/article/10.1088/2632-2153/abe808) (_Machine Learning: Science and Technology_, 2021).
## Requirements
- `conda>=3.7`
## Installation
The library itself has few dependencies (see [setup.py](setup.py)) with loose requirements.
To run the example training script we provide environment files under `examples/`.
Create a conda environment:
```sh
conda env create -f examples/conda.yml
```
Activate the environment:
```sh
conda activate paccmann_chemistry
```
Install in editable mode for development:
```sh
pip install -e .
```
## Example usage
In the `examples` directory is a training script [train_vae.py](./examples/train_vae.py) that makes use of `paccmann_chemistry`.
```console
(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/example_params.json), examples for other files can be downloaded from [here](https://ibm.box.com/v/paccmann-pytoda-data).
## References
If you use `paccmann_chemistry` in your projects, please cite the following:
```bib
@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}
}
```