  In recent years, machine learning (ML) methods have emerged as promising
alternatives for molecular docking, offering the potential for high accuracy
without incurring prohibitive computational costs. However, recent studies have
indicated that these ML models may overfit to quantitative metrics while
neglecting the physical constraints inherent in the problem. In this work, we
present Uni-Mol Docking V2, which demonstrates a remarkable improvement in
performance, accurately predicting the binding poses of 77+% of ligands in the
PoseBusters benchmark with an RMSD value of less than 2.0 {\AA}, and 75+%
passing all quality checks. This represents a significant increase from the 62%
achieved by the previous Uni-Mol Docking model. Notably, our Uni-Mol Docking
approach generates chemically accurate predictions, circumventing issues such
as chirality inversions and steric clashes that have plagued previous ML
models. Furthermore, we observe enhanced performance in terms of high-quality
predictions (RMSD values of less than 1.0 {\AA} and 1.5 {\AA}) and physical
soundness when Uni-Mol Docking is combined with more physics-based methods like
Uni-Dock. Our results represent a significant advancement in the application of
artificial intelligence for scientific research, adopting a holistic approach
to ligand docking that is well-suited for industrial applications in virtual
screening and drug design. The code, data and service for Uni-Mol Docking are
publicly available for use and further development in
https://github.com/dptech-corp/Uni-Mol.
