This algorithm is based on deep learning and a classical scoring function (Vina score) and is designed to optimize ligand conformations.
conda install -c conda-forge spyrmsd
conda install pytorch
conda install numpy pandas
Liangzhen Zheng, Shanghai Zelixir Biotech Company Ltd, [email protected]
Zechen Wang, Shandong University, [email protected]The algorithm simultaneously optimizes multiple poses of a ligand, which must be generated by the same docking program and placed in the same directory in PDBQT format. The PDBQT files for proteins and ligands can be generated by MGLTools. The detailed process is as follows.
pythonsh prepare_receptor4.py -r protein.pdb -U lps -o protein.pdbqt
pythonsh prepare_ligand4.py -l ligand.mol2 -U lps -o ligand.pdbqt
2. Prepare the input file with a pdb code, a protein PDBQT file and the directory where the ligand poses (PDBQT file) are located written on each line, separated by a space.
The content of the input file is as follows
1gpn ./samples/1gpn/1gpn_protein_atom_noHETATM.pdbqt samples/1gpn/decoys
1syi ./samples/1syi/1syi_protein_atom_noHETATM.pdbqt samples/1syi/decoys
bash run_pose_optimization.sh inputs.dat
Finally, the program outputs the optimized ligand conformation ("final_optimized_cnfr.pdb") and the final score. In addition, the conformation changes and scores during optimization are recorded in the "optimized_traj.pdb" and "opt_data.csv" files, respectively.