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/.
Liu et al. (2026) studied this question.