https://github.com/seonghwanseo/openpharmaco
Open-source protein-based pharmacophore modeling software
https://github.com/seonghwanseo/openpharmaco
deep-learning drug-discovery machine-learning pharmacophore-modelling protein-based-pharmacophore-modelling virtual-screening
Last synced: 6 months ago
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Open-source protein-based pharmacophore modeling software
- Host: GitHub
- URL: https://github.com/seonghwanseo/openpharmaco
- Owner: SeonghwanSeo
- License: mit
- Created: 2024-05-02T02:44:43.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2025-02-15T07:04:09.000Z (9 months ago)
- Last Synced: 2025-04-02T23:41:32.754Z (8 months ago)
- Topics: deep-learning, drug-discovery, machine-learning, pharmacophore-modelling, protein-based-pharmacophore-modelling, virtual-screening
- Language: Python
- Homepage:
- Size: 24.6 MB
- Stars: 21
- Watchers: 1
- Forks: 0
- Open Issues: 2
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# OpenPharmaco: Open-source Protein-based Pharmacophore Modeling Software
Open-Source software for Fully-automated Protein-based Pharmacophore Modeling and High-throughput Virtual Screening.
OpenPharmaco is currently powered by ***PharmacoNet: Accelerating Large-Scale Virtual Screening by Deep Pharmacophore Modeling***, developed by Seonghwan Seo, KAIST.
If you are deep learning researcher, please visit PharmacoNet [[github](https://github.com/SeonghwanSeo/PharmacoNet)]. It provides more functions.
If you have any problems or need help, please add an github issue.
You can get more information at [Wiki](https://github.com/SeonghwanSeo/OpenPharmaco/wiki).
\* Tested on Microsoft Window and Mac OS X (Apple Silicon).
## Quick Start
```bash
# Download Source Codes
git clone https://github.com/SeonghwanSeo/OpenPharmaco.git
# Create Environment
cd OpenPharmaco/
conda env create -f environment.yml
conda activate openph
pip install .
# Start
conda activate openph
openph # or openpharmaco
```
## Citation
Paper on [arxiv](https://arxiv.org/abs/2310.00681)
```
@article{seo2023pharmaconet,
title = {PharmacoNet: Accelerating Large-Scale Virtual Screening by Deep Pharmacophore Modeling},
author = {Seo, Seonghwan and Kim, Woo Youn},
journal = {arXiv preprint arXiv:2310.00681},
year = {2023},
url = {https://arxiv.org/abs/2310.00681},
}
```
## Future Plan
- Version 2.0.0
- Performance Improvement (Provisional: PharmacoNet v2)
- SMILES Input (Conformer-free inference)
- Verison 2.1.0:
- Binding Site Detection for Apo Protein Structures
- Pharmacophore Customizing
- Version 3
- Binding Pose Prediction
## Reference
- [PyTorch](https://pytorch.org)
- [NumPy](https://numpy.org)
- [Biopython](http://biopython.org)
- [Open Babel](http://openbabel.org)
- [Open-Source PyMOL](http://pymol.org) ([github](https://github.com/schrodinger/pymol-open-source))
- [PyQt5](https://www.riverbankcomputing.com/software/pyqt/)