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Efficient and accurate large library ligand docking with KarmaDock | Nature Computational Science

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Article Published: 21 September 2023 Efficient and accurate large library ligand docking with KarmaDock Xujun Zhang, Odin Zhang, Chao Shen, Wanglin Qu, Shicheng Chen, Hanqun Cao, Yu Kang, Zhe Wang, Ercheng Wang, Jintu Zhang, Yafeng Deng, Furui Liu, Tianyue Wang, Hongyan Du, Langcheng Wang, Peichen Pan, Guangyong Chen, Chang-Yu Hsieh & Tingjun Hou 

Nature Computational Science volume  3,  pages 789–804 (2023) Cite this article

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Abstract

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).

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Additional access options: Log in Learn about institutional subscriptions Read our FAQs Contact customer support Similar content being viewed by others FeatureDock for protein-ligand docking guided by physicochemical feature-based local environment learning using transformer Article Open access 04 March 2025 Sequence-based virtual screening using transformers Article Open access 28 July 2025 An artificial intelligence accelerated virtual screening platform for drug discovery Article Open access 05 September 2024 Data availability

The raw datasets26,27,28,29 are available at http://pdbbind.org.cn/index.php, https://github.com/devalab/Apobind and http://www.pharmchem.uni-tuebingen.de/dekois/data/DEKOIS2.0library/DEKOIS2.0library.rar. The prepared datasets42,43,44 are available at https://zenodo.org/record/7788083, https://zenodo.org/record/8211452 and https://zenodo.org/record/8131256. PDB IDs 1S38, 1SQA, 4JXS, 1PS3, 3DXG, 3D4Z, 4CLI, 4JSZ and 4CTB are available in the Protein Data Bank (https://www.rcsb.org/)37. Source data are available with this paper.

Code availability

The source code is available at Zenodo (https://zenodo.org/record/8211513)45 and GitHub (https://github.com/schrojunzhang/KarmaDock).

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Acknowledgements

This work was financially supported by the National Key Research and Development Program of China (2022YFF1203000), the National Natural Science Foundation of China (22220102001, 82204279 and 22007082), the Natural Science Foundation of Zhejiang Province (LD22H300001 and LQ21B030013) and Fundamental Research Funds for the Central Universities (226-2022-00220). We also thank L. Xu at Jiangsu University of Technology for preparing all the compounds used in this study based on the Glide module in Schrödinger software, which substantially contributed to our research.

Author information Author notes

These authors contributed equally: Xujun Zhang, Odin Zhang.

Authors and Affiliations

Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, Zhejiang, China

Xujun Zhang, Odin Zhang, Chao Shen, Wanglin Qu, Shicheng Chen, Yu Kang, Zhe Wang, Jintu Zhang, Tianyue Wang, Hongyan Du, Peichen Pan, Chang-Yu Hsieh & Tingjun Hou

Department of Mathematics, Chinese University of Hong Kong, Hong Kong, China

Hanqun Cao

Zhejiang Lab, Hangzhou, Zhejiang, China

Ercheng Wang, Furui Liu & Guangyong Chen

Hangzhou Carbonsilicon AI Technology Co., Ltd, Hangzhou, Zhejiang, China

Yafeng Deng

Department of Pathology, New York University Medical Center, New York, NY, USA

Langcheng Wang

Contributions

X.Z., O.Z. and C.S. developed this method, analyzed the data and wrote the manuscript. W.Q. and S.C. bought the compounds and measured their IC50 values. H.C., Y.K., Z.W., E.W., J.Z., Y.D., F.L., T.W., H.D. and L.W. evaluated and interpreted the results and wrote the manuscript. P.P., G.C., C.-Y.H. and T.H. conceived and supervised the project, interpreted the results and wrote the manuscript. All authors read and approved the final manuscript.

Corresponding authors

Correspondence to Peichen Pan, Guangyong Chen, Chang-Yu Hsieh or Tingjun Hou.

Ethics declarations Competing interests

The authors declare no competing interests.

Peer review Peer review information

Nature Computational Science thanks Matthew Holcomb and Shina Kamerlin for their contribution to the peer review of this work. Primary Handling Editor: Kaitlin McCardle, in collaboration with the Nature Computational Science team.

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Supplementary information Supplementary Information (download PDF )

Supplementary sections 1–5, Figs. 1–3 and Tables 1–7.

Reporting Summary (download PDF ) Supplementary Data 1 (download XLSX )

The docking power and screening power of various models on CASF 2016. Tool rank denotes the ranking of the tools based on their performance. Success rate: a metric used for evaluating the docking power of SFs, represents the ratio of successfully docked complexes (r.m.s.d. ≤ 2 Å) to all the tested complexes; Model type signifies the type of algorithm or methodology used in the tool (for example, DL for deep learning models, DK for traditional docking program and HB for hybrid models); EF 1% (enrichment factor), a metric used for assessing the screening power of SFs, represents the enrichment of active ligands in top 1% scored compounds.

Supplementary Data 2 (download CSV )

Interaction reproduction. Each row represents a specific interaction type for a given structure, the post-processing type applied to the data, the repeat number and the observed reproduction rate of the interaction.

Supplementary Data 3 (download CSV )

Pose refinement results for structures from PDBBind core set. The data provides r.m.s.d. values indicating the accuracy of predicted poses generated by various methods and their respective post-processing treatments.

Source data Source Data Fig. 2 (download CSV )

Statistical source data for Fig. 2 showing the success rate, docking speed, heavy atoms number, rotatable bonds number for PDBBind refined set.

Source Data Fig. 3 (download CSV )

Statistical source data for Fig. 3 showing the success rate, docking speed, heavy atoms number, rotatable bonds number for PDBBind refined set.

Source Data Fig. 4 (download CSV )

Statistical source data for Fig. 4 showing the conformation errors.

Source Data Fig. 5 (download XLSX )

Statistical source data for Fig. 5 showing the accuracy and screening power of KarmaDock on DEKOIS 2.0.

Source Data Fig. 6 (download XLSX )

Statistical source data for Fig. 6 showing the Inhibition Activity of compounds on BaF3/CLIP1-LTK cells.

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Zhang, X., Zhang, O., Shen, C. et al. Efficient and accurate large library ligand docking with KarmaDock. Nat Comput Sci 3, 789–804 (2023). https://doi.org/10.1038/s43588-023-00511-5

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Received 20 March 2023

Accepted 08 August 2023

Published 21 September 2023

Version of record 21 September 2023

Issue date September 2023

DOI https://doi.org/10.1038/s43588-023-00511-5

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