A comprehensive accurate assessment approach for deep learning based molecular docking
Project description
CompassDock 🧭
Navigating Future Drugs with CompassDock 🧭
The CompassDock framework is a comprehensive and accurate assessment approach for deep learning-based molecular docking. It evaluates key factors such as the physical and chemical properties, bioactivity favorability of ligands, strain energy, number of protein-ligand steric clashes, binding affinity, and protein-ligand interaction types.
Quickstart for CompassDock
conda create --name CompassDock python=3.11 -c conda-forge
conda install -c ostrokach-forge reduce
conda install -c conda-forge openbabel
conda install -c conda-forge datamol
pip install compassdock
pip install pyg_lib torch_scatter torch_sparse torch_cluster torch_spline_conv -f https://data.pyg.org/whl/torch-2.3.0+cu121.html
pip install "fair-esm @ git+https://github.com/asarigun/esm.git"
pip install "dllogger @ git+https://github.com/NVIDIA/dllogger.git"
pip install "openfold @ git+https://github.com/asarigun/openfold.git"
from compassdock import CompassDock
cd = CompassDock()
cd.recursive_compassdocking(
protein_path = 'example/proteins/1a46_protein_processed.pdb',
protein_sequence = None,
ligand_description = 'CCCCC(NC(=O)CCC(=O)O)P(=O)(O)OC1=CC=CC=C1',
complex_name = 'complex_1',
molecule_name = 'molecule_1',
out_dir = 'results',
compass_all = False)
results = cd.results()
print(results)
Protein Sequance - Ligand Docking
from compassdock import CompassDock
# Initialize CompassDock
cd = CompassDock()
# Perform docking using the provided protein and ligand information
cd.recursive_compassdocking(
protein_path = None,
protein_sequence = 'GIQSYCTPPYSVLQDPPQPVV',
ligand_description = 'CCCCC(NC(=O)CCC(=O)O)P(=O)(O)OC1=CC=CC=C1',
complex_name = 'complex_1',
molecule_name = 'molecule_1',
out_dir = 'results',
compass_all = False,
max_redocking_step = 1)
# Retrieve and print the docking results
results = cd.results()
print(results)
More examples can be found in !
CompassDock 🧭 in Fine-Tuning Mode
For instructions on how to use Fine-Tuning Mode, please refer to the previous branch
Citation
Please cite the following paper if you use this code/repository in your research:
@article{sarigun2024compass,
title={Compass: A Comprehensive Tool for Accurate and Efficient Molecular Docking in Inference and Fine-Tuning},
author={Sarigun, Ahmet and Franke, Vedran and Akalin, Altuna},
journal={arXiv preprint arXiv:2406.06841},
year={2024}
}
Acknowledgements
We extend our deepest gratitude to the following teams for open-sourcing their valuable Repos:
- DiffDock Team (version 2023 & 2024),
- AA-score Team,
- PoseCheck Team
Project details
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
Built Distribution
File details
Details for the file compassdock-0.1.3.tar.gz
.
File metadata
- Download URL: compassdock-0.1.3.tar.gz
- Upload date:
- Size: 212.1 kB
- Tags: Source
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/5.1.1 CPython/3.11.8
File hashes
Algorithm | Hash digest | |
---|---|---|
SHA256 | b3d018aace61bc8af4c4aec03e3607d922f7fd8f0205630a73cb009eb5861730 |
|
MD5 | fd2bd5338cbc6c4463e869ca8c1ebbeb |
|
BLAKE2b-256 | d075545ae903e9a3a96147297bfe7621cb509b389fee296a8ae7c1f0c955eb07 |
File details
Details for the file compassdock-0.1.3-py3-none-any.whl
.
File metadata
- Download URL: compassdock-0.1.3-py3-none-any.whl
- Upload date:
- Size: 255.5 kB
- Tags: Python 3
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/5.1.1 CPython/3.11.8
File hashes
Algorithm | Hash digest | |
---|---|---|
SHA256 | 19740ddc554effb9d298f507d0c0873c4f3957c42cac11d2316b8ca2b07a5254 |
|
MD5 | 0e95b8b0a6b13c44101e40d1e05ba9ee |
|
BLAKE2b-256 | 59616b21a0b175db0b5876936849cc89c57ccb7fae96521818d0db857edd6ed1 |