# KarmaDock **Repository Path**: dhxia007/KarmaDock ## Basic Information - **Project Name**: KarmaDock - **Description**: 分子对接，深度学习 - **Primary Language**: Unknown - **License**: MIT - **Default Branch**: main - **Homepage**: None - **GVP Project**: No ## Statistics - **Stars**: 1 - **Forks**: 0 - **Created**: 2024-02-14 - **Last Updated**: 2024-02-14 ## Categories & Tags **Categories**: Uncategorized **Tags**: 分子对接, 深度学习, 图神经网络 ## README # KarmaDock: a deep learning paradigm for ultra-large library docking with fast speed and high accuracy ![](https://github.com/schrojunzhang/KarmaDock/blob/main/result1.gif) ## Contents - [Overview](#overview) - [Software Requirements](#software-requirements) - [Installation Guide](#installation-guide) - [Demo: Ligand docking](#demo1--ligand-docking-on-pdbbind-core-set) - [Demo: Virtual screening](#demo2--virtual-screening-on-dekois-20) ## Overview Ligand docking is one of the core technologies in structure-based virtual screening for drug discovery. However, conventional docking tools and existing deep learning tools may suffer from limited performance in terms of speed, pose quality and binding affinity accuracy. Here we propose KarmaDock, a deep learning approach for ligand docking that integrates the functions of docking acceleration, binding pose generation and correction, and binding strength estimation. The three-stage model consists of the following components: (1) encoders for the protein and ligand to learn the representations of intramolecular interactions; (2) E(n) equivariant graph neural networks with self-attention to update the ligand pose based on both protein–ligand and intramolecular interactions, followed by post-processing to ensure chemically plausible structures; (3) a mixture density network for scoring the binding strength. KarmaDock was validated on four benchmark datasets and tested in a real-world virtual screening project that successfully identified experiment-validated active inhibitors of leukocyte tyrosine kinase (LTK). If you find it useful, please cite: **Efficient and accurate large library ligand docking with KarmaDock** Zhang, Xujun#; Zhang, Odin#; Shen, Chao; Qu, Wanglin; Chen, Shicheng; Cao, Hanqun; Kang, Yu; Wang, Zhe; Wang, Ercheng; Zhang, Jintu; Deng, Yafeng; Liu, Furui; Wang, Tianyue; Du, Hongyan; Wang, Langcheng; Pan, Peichen*; Chen, Guangyong*; Hsieh, Chang-Yu*; Hou, Tingjun*. *Published in:* Nature Computational Science, 2023, Vol. 3, No. 9, pp. 789-804. *DOI:* [10.1038/s43588-023-00511-5](https://www.nature.com/articles/s43588-023-00511-5) ## Software Requirements ### OS Requirements The package development version is tested on *Linux: Ubuntu 18.04* operating systems. ### Python Dependencies Dependencies for KarmaDock: ``` pytorch pyg rdkit=2022.09.1 (important!!!) mdanalysis prody ``` ## Installation Guide ### download this repo ``` git clone https://github.com/schrojunzhang/KarmaDock.git ``` ### install karmadock_env you can install the env via yaml file ``` cd KarmaDock conda env create -f karmadock_env.yaml ``` or you can download the [conda-packed file](https://zenodo.org/record/7788732/files/karmadock_env.tar.gz?download=1), and then unzip it in `${anaconda install dir}/anaconda3/envs`. `${anaconda install dir}` represents the dir where the anaconda is installed. For me, ${anaconda install dir}=/root . ``` mkdir ${anaconda install dir}/anaconda3/envs/karmadock tar -xzvf karmadock.tar.gz -C ${anaconda install dir}/anaconda3/envs/karmadock conda activate karmadock ``` ## Demo1 & ligand docking on PDBBind core set Assume that the project is at `/root` and therefore the project path is /root/KarmaDock. ### 1. Download PDBBind dataset You can download the PDBBind 2020 core set without preprocessing from the [PDBBind website](http://pdbbind.org.cn/index.php) OR you can download [the version](https://zenodo.org/record/7788083/files/pdbbind2020_core_set.zip?download=1) where protein files were prepared by Schrodinger. ``` cd /root/KarmaDock wget https://zenodo.org/record/7788083/files/pdbbind2020_core_set.zip?download=1 unzip -q pdbbind2020_core_set.zip?download=1 ``` ### 2. Preprocess PDBBind data The purpose of this step is to identify residues that are within a 12Å radius of any ligand atom and use them as the pocket of the protein. The pocket file (xxx_pocket_ligH12A.pdb) will also be saved on the `complex_file_dir`. ``` cd /root/KarmaDock/utils python -u pre_processing.py --complex_file_dir ~/your/PDBBindDataset/path ``` e.g., ``` cd /root/KarmaDock/utils python -u pre_processing.py --complex_file_dir /root/KarmaDock/pdbbind2020_core_set ``` ### 3. Generate graphs based on protein-ligand complexes This step will generate graphs for protein-ligand complexes and save them (*.dgl) to `graph_file_dir`. ``` cd /root/KarmaDock/utils python -u generate_graph.py --complex_file_dir ~/your/PDBBindDataset/path --graph_file_dir ~/the/directory/for/saving/graph ``` e.g., ``` cd /root/KarmaDock/utils python -u generate_graph.py --complex_file_dir /root/KarmaDock/pdbbind2020_core_set --graph_file_dir /root/KarmaDock/pdbbind_graph ``` ### 4. ligand docking This step will perform ligand docking (predict binding poses and binding strengthes) based on the graphs. (finished in about 0.5 min) ``` cd /root/KarmaDock/utils python -u ligand_docking.py --graph_file_dir ~/the/directory/for/saving/graph --model_file ~/path/of/trained/model/parameters --out_dir ~/path/for/recording/BindingPoses&DockingScores --docking Ture/False whether generating binding poses --scoring Ture/False whether predict binding affinities --correct Ture/False whether correct the predicted binding poses --batch_size 64 --random_seed 2023 ``` e.g., ``` cd /root/KarmaDock/utils python -u ligand_docking.py --graph_file_dir /root/KarmaDock/pdbbind_graph --model_file /root/KarmaDock/trained_models/karmadock_screening.pkl --out_dir /root/KarmaDock/pdbbind_result --docking True --scoring True --correct True --batch_size 64 --random_seed 2023 ``` ## Demo2 & virtual screening on DEKOIS 2.0 Assume that the project is at `/root` and therefore the project path is /root/KarmaDock. ### 1. Download DEKOIS dataset You can download the DEKOIS 2.0 dataset without preprocessing from the [DEKOIS website](http://www.pharmchem.uni-tuebingen.de/dekois/) OR you can download [the version](https://zenodo.org/record/8131256/files/DEKOIS2.zip?download=1) where protein files were prepared by Schrodinger, glide-docked poses were provided. ``` cd /root/KarmaDock wget https://zenodo.org/record/8131256/files/DEKOIS2.zip?download=1 unzip -q DEKOIS2.zip?download=1 ``` ### 2. virtual screening This step will perform virtual screening for a specific target PDK1 (predict binding poses and binding strengthes). ###### (1) CPU and GPU machines (faster): You can run the following command on CPUs before performing virtual screening (generate graphs in advance) ``` cd /root/KarmaDock/utils python -u virtual_screening_pipeline.py --mode generate_graph --ligand_smi ~/the/directory/for/ligand/library/smi --protein_file ~/the/directory/for/target/protein/pdb --crystal_ligand_file ~/the/directory/for/crystal/ligand/mol2/for/binding/pocket --graph_dir ~/the/directory/for/saving/ligand/graphs --random_seed 2023 ``` e.g., ``` cd /root/KarmaDock/utils python -u virtual_screening_pipeline.py --ligand_smi /root/KarmaDock/DEKOIS2/pdk1/active_decoys.smi --protein_file /root/KarmaDock/DEKOIS2/pdk1/protein/pdk1_protein.pdb --crystal_ligand_file /root/KarmaDock/DEKOIS2/pdk1/protein/pdk1_ligand.mol2 --graph_dir /root/KarmaDock/DEKOIS2/pdk1/karmadock_liggraph --random_seed 2023 ``` Then, you can run the following command on GPUs to perform virtual screening (predict binding poses and binding strengthes) ``` cd /root/KarmaDock/utils python -u virtual_screening_pipeline.py --mode vs --protein_file ~/the/directory/for/target/protein/pdb --crystal_ligand_file ~/the/directory/for/crystal/ligand/mol2/for/binding/pocket --graph_dir ~/the/directory/for/saving/ligand/graphs --out_dir ~/path/for/recording/BindingPoses&DockingScores --score_threshold 50 --batch_size 64 --random_seed 2023 --out_uncoorected --out_corrected ``` e.g., ``` cd /root/KarmaDock/utils python -u virtual_screening_pipeline.py --protein_file /root/KarmaDock/DEKOIS2/pdk1/protein/pdk1_protein.pdb --crystal_ligand_file /root/KarmaDock/DEKOIS2/pdk1/protein/pdk1_ligand.mol2 --graph_dir /root/KarmaDock/DEKOIS2/pdk1/karmadock_liggraph --out_dir /root/KarmaDock/DEKOIS2/pdk1/karmadocked --score_threshold 50 --batch_size 64 --random_seed 2023 --out_uncoorected --out_corrected ``` ###### (2) GPU machines (slower but more convinent): For pure GPU machines, you can run the following command to perform virtual screening (generate graphs on the fly) ``` cd /root/KarmaDock/utils python -u virtual_screening.py --ligand_smi ~/the/directory/for/ligand/library/smi --protein_file ~/the/directory/for/target/protein/pdb --crystal_ligand_file ~/the/directory/for/crystal/ligand/mol2/for/binding/pocket --out_dir ~/path/for/recording/BindingPoses&DockingScores --score_threshold 50 --batch_size 64 --random_seed 2023 --out_uncoorected --out_corrected ``` e.g., ``` cd /root/KarmaDock/utils python -u virtual_screening.py --ligand_smi /root/KarmaDock/DEKOIS2/pdk1/active_decoys.smi --protein_file /root/KarmaDock/DEKOIS2/pdk1/protein/pdk1_protein.pdb --crystal_ligand_file /root/KarmaDock/DEKOIS2/pdk1/protein/pdk1_ligand.mol2 --out_dir /root/KarmaDock/DEKOIS2/pdk1/karmadocked --score_threshold 50 --batch_size 64 --random_seed 2023 --out_uncoorected --out_corrected ```