https://github.com/paccmann/paccmann_generator
Generative models for transcriptomic-driven or protein-driven molecular design (PaccMann^RL).
https://github.com/paccmann/paccmann_generator
deep-reinforcement-learning drug-discovery generative-model molecule-generation vae
Last synced: 7 months ago
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Generative models for transcriptomic-driven or protein-driven molecular design (PaccMann^RL).
- Host: GitHub
- URL: https://github.com/paccmann/paccmann_generator
- Owner: PaccMann
- License: mit
- Created: 2019-11-04T19:55:40.000Z (over 6 years ago)
- Default Branch: master
- Last Pushed: 2024-05-22T20:36:24.000Z (almost 2 years ago)
- Last Synced: 2025-04-10T19:39:32.505Z (11 months ago)
- Topics: deep-reinforcement-learning, drug-discovery, generative-model, molecule-generation, vae
- Language: Python
- Homepage:
- Size: 1.3 MB
- Stars: 11
- Watchers: 3
- Forks: 7
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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# paccmann_generator
Multimodal generative models for PaccMannRL.
`paccmann_generator` is a package for conditional molecular design, with examples of molecule generation against gene expression profiles or protein targets.
For example, see our papers:
- [_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). In there, we use methods from deep reinforcement learning to bias a molecular generator to produce molecules that exhibit low IC50 against certain cell lines (code in this repo).
- [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). In there, we use the same principle to bias a molecular generator to produce molecules that have high binding affinity against certain protein targets (code in this repo).
## 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/IC50/conda.yml
```
Activate the environment:
```sh
conda activate paccmann_generator
```
Install in editable mode for development:
```sh
git checkout 0.0.1 # Needed only for IC50 example (for affinity example skip this line)
pip install -e .
```
## Example usage
In the `examples/IC50` directory is a training script [train_paccmann_rl.py](./examples/IC50/train_paccmann_rl.py) that makes use of `paccmann_generator`.
```console
(paccmann_generator) $ python examples/IC50/train_paccmann_rl.py -h
usage: train_paccmann_rl.py [-h]
mol_model_path omics_model_path ic50_model_path
smiles_language_path omics_data_path params_path
model_name site
PaccMann^RL training script
positional arguments:
mol_model_path Path to chemistry model
omics_model_path Path to omics model
ic50_model_path Path to pretrained ic50 model
smiles_language_path Path to SMILES language object
omics_data_path Omics data path to condition generation
params_path Model params json file directory
model_name Name for the trained model.
site Name of the cancer site for conditioning.
optional arguments:
-h, --help show this help message and exit
```
`params_filepath` could point to [examples/IC50/example_params.json](examples/IC50/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_generator` 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}
}
```