CarsiDock-Cov is a deep learning-guided protocol for automated covalent docking. It inherits the core components of CarsiDock, but makes a few changes to facilitate the applicability of the whole protocol for covalent docking.
rdkit==2022.9.4
ProDy==2.1.0
pytorch-lightning==1.5.0
python==3.8.19
pytorch==1.12.1+cu116
transformers==4.15.0
spyrmsd==0.6.0
joblib==1.4.2
torchmetrics==1.4.1
numpy==1.24.4
scipy==1.10.1
torch-scatter==2.1.2
scikit-learn==1.3.2
pandas==2.0.3
The datasets employed for the validation of our approach have been shared at Zenodo.
The code of this repository is licensed under Aapache Licence 2.0. CarsiDock-Cov directly ueses the model trained in CarsiDock to predict the protein-ligand distance matrices, so the use of the CarsiDock model weights should follow the Model License. CarsiDock weights are completely open for academic research, please contact bd@carbonsilicon.ai for commercial use.
If you agree to the above license, please download checkpoints from the corresponding repository and put them in the checkpoints folder.
# Docking using the SMILES as input
python run_carsidockcov.py -p ./example_data/2qlq_p.pdb -rl ./example_data/2qlq_l.sdf \
-smi "CN(C)C/C=C/C(=O)Nc1ccc2ncnc(Nc3cccc(Br)c3)c2c1" -covres "A:CYS:345" \
-rectype 'Michael Addition' -remove_dummyatom -remain_pocket -o './example_data/2qlq_out'
# Docking using the sdf as input
python run_carsidockcov.py -p ./example_data/2qlq_p.pdb -rl ./example_data/2qlq_l.sdf \
-l ./example_data/2qlq_l_orig.sdf -covres "A:CYS:345" \
-rectype 'Michael Addition' -remove_dummyatom -remain_pocket -o './example_data/2qlq_out'
# Virtual screening
python run_carsidockcov_screening.py -p ./example_data/2qlq_p.pdb -rl ./example_data/2qlq_l.sdf \
-l ./example_data/covvs.sdf -covres "A:CYS:345" \
-rectype 'Michael Addition' -remove_dummyatom -remain_pocket -o './example_data/2qlq_out'