{"id":22582457,"url":"https://github.com/paccmann/paccmann_chemistry","last_synced_at":"2025-04-10T19:11:58.790Z","repository":{"id":47001037,"uuid":"219302308","full_name":"PaccMann/paccmann_chemistry","owner":"PaccMann","description":"Generative models of chemical data for PaccMann^RL","archived":false,"fork":false,"pushed_at":"2023-06-02T07:12:28.000Z","size":126,"stargazers_count":14,"open_issues_count":0,"forks_count":8,"subscribers_count":5,"default_branch":"master","last_synced_at":"2025-03-24T16:53:19.471Z","etag":null,"topics":["deep-learning","drug-discovery","generative-model","molecule-generation","vae"],"latest_commit_sha":null,"homepage":"","language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/PaccMann.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null}},"created_at":"2019-11-03T13:06:32.000Z","updated_at":"2024-10-08T23:37:04.000Z","dependencies_parsed_at":"2023-01-19T15:15:09.564Z","dependency_job_id":null,"html_url":"https://github.com/PaccMann/paccmann_chemistry","commit_stats":null,"previous_names":[],"tags_count":6,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/PaccMann%2Fpaccmann_chemistry","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/PaccMann%2Fpaccmann_chemistry/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/PaccMann%2Fpaccmann_chemistry/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/PaccMann%2Fpaccmann_chemistry/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/PaccMann","download_url":"https://codeload.github.com/PaccMann/paccmann_chemistry/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":248279802,"owners_count":21077408,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["deep-learning","drug-discovery","generative-model","molecule-generation","vae"],"created_at":"2024-12-08T06:10:18.374Z","updated_at":"2025-04-10T19:11:58.770Z","avatar_url":"https://github.com/PaccMann.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"[![Build Status](https://github.com/PaccMann/paccmann_chemistry/actions/workflows/build.yml/badge.svg)](https://github.com/PaccMann/paccmann_chemistry/actions/workflows/build.yml)\n[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)\n[![Gradio demo](https://img.shields.io/website-up-down-green-red/https/hf.space/gradioiframe/GT4SD/paccmann_rl/+.svg?label=demo%20status)](https://huggingface.co/spaces/GT4SD/paccmann_rl)\n\n\n# paccmann_chemistry\n\nGenerative models of chemical data for PaccMann\u003csup\u003eRL\u003c/sup\u003e. For example, a SMILES/SELFIES VAE using stack-augmented GRUs in both encoder and decoder. For details, see for example:\n\n- [![DOI:10.1016/j.isci.2021.102269](http://img.shields.io/badge/DOI-10.1016/j.isci.2021.102269-094573.svg)](https://doi.org/10.1016/j.isci.2021.102269) [PaccMann\u003csup\u003eRL\u003c/sup\u003e: 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). \n\n- [![DOI:10.1088/2632-2153/abe808](http://img.shields.io/badge/DOI-10.1088/2632/2153/abe808-C1D4F4.svg)](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).\n\n\n\n\n\n## Requirements\n\n- `conda\u003e=3.7`\n\n## Installation\n\nThe library itself has few dependencies (see [setup.py](setup.py)) with loose requirements. \nTo run the example training script we provide environment files under `examples/`.\n\nCreate a conda environment:\n\n```sh\nconda env create -f examples/conda.yml\n```\n\nActivate the environment:\n\n```sh\nconda activate paccmann_chemistry\n```\n\nInstall in editable mode for development:\n\n```sh\npip install -e .\n```\n\n## Example usage\n\nIn the `examples` directory is a training script [train_vae.py](./examples/train_vae.py) that makes use of `paccmann_chemistry`.\n\n```console\n(paccmann_chemistry) $ python examples/train_vae.py -h\nusage: train_vae.py [-h]\n                    train_smiles_filepath test_smiles_filepath\n                    smiles_language_filepath model_path params_filepath\n                    training_name\n\nChemistry VAE training script.\n\npositional arguments:\n  train_smiles_filepath\n                        Path to the train data file (.smi).\n  test_smiles_filepath  Path to the test data file (.smi).\n  smiles_language_filepath\n                        Path to SMILES language object.\n  model_path            Directory where the model will be stored.\n  params_filepath       Path to the parameter file.\n  training_name         Name for the training.\n\noptional arguments:\n  -h, --help            show this help message and exit\n```\n\n`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).\n\n## References\n\nIf you use `paccmann_chemistry` in your projects, please cite the following:\n\n```bib\n@article{born2021datadriven,\n  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},\n  doi = {10.1088/2632-2153/abe808},\n  issn = {2632-2153},\n  journal = {Machine Learning: Science and Technology},\n  number = {2},\n  pages = {025024},\n  title = {{Data-driven molecular design for discovery and synthesis of novel ligands: a case study on SARS-CoV-2}},\n  url = {https://iopscience.iop.org/article/10.1088/2632-2153/abe808},\n  volume = {2},\n  year = {2021}\n}\n\n@article{born2021paccmannrl,\n  title = {PaccMann\\textsuperscript{RL}: De novo generation of hit-like anticancer molecules from transcriptomic data via reinforcement learning},\n  journal = {iScience},\n  volume = {24},\n  number = {4},\n  pages = {102269},\n  year = {2021},\n  issn = {2589-0042},\n  doi = {https://doi.org/10.1016/j.isci.2021.102269},\n  url = {https://www.cell.com/iscience/fulltext/S2589-0042(21)00237-6},\n  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}\n}\n```\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fpaccmann%2Fpaccmann_chemistry","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fpaccmann%2Fpaccmann_chemistry","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fpaccmann%2Fpaccmann_chemistry/lists"}