CB-Dock3: an enhanced web server for protein–ligand blind docking

Elucidating protein-ligand interactions is pivotal for understanding biological mechanisms and accelerating drug discovery. Blind docking, which identifies binding sites without prior knowledge, has become an indispensable computational strategy for analyzing the surge of protein structures generated by Cryo-EM and AI-based prediction tools like AlphaFold3. Our previous server, CB-Dock2, has been widely adopted by the global research community, averaging over 1000 daily submissions since July 2022 due to its accuracy and user-friendliness. Building on this foundation and incorporating extensive user feedback, we present CB-Dock3, a substantially enhanced platform. Key upgrades include a refined docking engine, an expanded template library, and support for diverse file formats. Benchmark evaluations on CASF-2016 demonstrate that CB-Dock3 achieves a success rate of 67.4% (RMSD ≤ 2.0 Å), representing a 10.6 percentage-point absolute improvement over its predecessor and outperforming other popular blind docking tools. Additionally, CB-Dock3 introduces critical new features driven by community needs: support for user-defined docking regions to handle large complexes, and a metal-aware protocol that explicitly retains essential metal ions and cofactors during simulation. CB-Dock3 stands as an accurate, rapid, and accessible resource for the scientific community, freely available at https://cadd.labshare.cn/cb-dock3/.

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Publication Details

Journal
Nucleic Acids Research
Published
2026-04-25
DOI
https://doi.org/10.1093/nar/gkag417
Primary Topic
Computational Drug Discovery Methods
Type
article
Field-Weighted Citation Impact
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article

CB-Dock3: an enhanced web server for protein–ligand blind docking

Zhi-xiong Xiao, Ding Ji, Fanjie Zong, Xiaoman Xiong et al.
Nucleic Acids Research
Computational Drug Discovery Methods
article

CB-Dock3: an enhanced web server for protein–ligand blind docking

Zhi-xiong Xiao, Ding Ji, Fanjie Zong, Xiaoman Xiong, Yang Cao, Yang Liu, Jianhong Gan
article en

Abstract

Elucidating protein-ligand interactions is pivotal for understanding biological mechanisms and accelerating drug discovery. Blind docking, which identifies binding sites without prior knowledge, has become an indispensable computational strategy for analyzing the surge of protein structures generated by Cryo-EM and AI-based prediction tools like AlphaFold3. Our previous server, CB-Dock2, has been widely adopted by the global research community, averaging over 1000 daily submissions since July 2022 due to its accuracy and user-friendliness. Building on this foundation and incorporating extensive user feedback, we present CB-Dock3, a substantially enhanced platform. Key upgrades include a refined docking engine, an expanded template library, and support for diverse file formats. Benchmark evaluations on CASF-2016 demonstrate that CB-Dock3 achieves a success rate of 67.4% (RMSD ≤ 2.0 Å), representing a 10.6 percentage-point absolute improvement over its predecessor and outperforming other popular blind docking tools. Additionally, CB-Dock3 introduces critical new features driven by community needs: support for user-defined docking regions to handle large complexes, and a metal-aware protocol that explicitly retains essential metal ions and cofactors during simulation. CB-Dock3 stands as an accurate, rapid, and accessible resource for the scientific community, freely available at https://cadd.labshare.cn/cb-dock3/.

Nucleic Acids Research
Sichuan University (CN), Sichuan Agricultural University (CN), West China Hospital of Sichuan University (CN), Sichuan Center for Disease Control and Prevention (CN)
Openalex Percentile: Top 5%
Computational Drug Discovery Methods
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