# Uni-Mol Docking V2: Towards Realistic and Accurate Binding Pose Prediction

arXiv: https://arxiv.org/abs/2405.11769 · HTML: https://mlsanity.com/paper/2405.11769/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 0 of its 12 references that are in the map.
- First posted on arXiv 20 May 2024.

## Built on by (most cited first, 10 of 10)

- [Unified Cross-Scale 3D Generation and Understanding via Autoregressive Modeling](https://mlsanity.com/paper/2503.16278/) (2025)
- [PoseX: AI Defeats Physics Approaches on Protein-Ligand Cross Docking](https://mlsanity.com/paper/2505.01700/) (2025)
- [3DMolFormer: A Dual-channel Framework for Structure-based Drug Discovery](https://mlsanity.com/paper/2502.05107/) (2025)
- [SigmaDock: Untwisting Molecular Docking With Fragment-Based SE(3) Diffusion](https://mlsanity.com/paper/2511.04854/) (2025)
- [Reaction-conditioned De Novo Enzyme Design with GENzyme](https://mlsanity.com/paper/2411.16694/) (2024)
- [MC-GNNAS-Dock: Multi-criteria GNN-based Algorithm Selection for Molecular Docking](https://mlsanity.com/paper/2509.26377/) (2025)
- [Matcha: Multi-Stage Riemannian Flow Matching for Accurate and Physically Valid Molecular Docking](https://mlsanity.com/paper/2510.14586/) (2025)
- [Molecular Embedding-Based Algorithm Selection in Protein-Ligand Docking](https://mlsanity.com/paper/2512.02328/) (2025)
- [PharMolixFM: All-Atom Foundation Models for Molecular Modeling and Generation](https://mlsanity.com/paper/2503.21788/) (2025)
- [ChemATP: A Training-Free Chemical Reasoning Framework for Large Language Models](https://mlsanity.com/paper/2512.19240/) (2025)

## Abstract (arXiv)

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.