PoseX: AI Defeats Physics-based Methods on Protein Ligand Cross-Docking

Yize Jiang, Xinze Li, Yuanyuan Zhang, Jin Han, Youjun Xu, Ayush Pandit, ZAIXI ZHANG, Mengdi Wang, Mengyang Wang, Chong Liu, Guang Yang, Yejin Choi, Wu-Jun Li, Tianfan Fu, Fang Wu, Junhong Liu

Keywords: AI docking, AI co-folding, protein-ligand interaction

TL;DR: a comprehensive benchmarking of various docking methods on a new curated dataset for self-docking and cross-docking

Abstract: Recently, significant progress has been made in protein-ligand docking, especially in deep learning-based methods, and some benchmarks were proposed, such as PoseBench and PLINDER. However, these studies typically focus on the self-docking scenario, which is less practical in real-world applications. Moreover, some studies involve heavy frameworks requiring extensive training, posing challenges for convenient and efficient assessment of docking methods. To address these gaps, we introduce PoseX, an open-source benchmark designed to evaluate both self-docking and cross-docking, enabling a practical and comprehensive assessment of algorithmic advances. Specifically, we curated a novel dataset comprising 718 entries for self-docking and 1,312 entries for cross-docking; secondly, we incorporated 23 docking methods in three methodological categories, including physics-based methods (e.g., Schrödinger Glide), AI docking methods (e.g., DiffDock) and AI co-folding methods (e.g., AlphaFold3); thirdly, we developed a relaxation method for post-processing to minimize conformational energy and refine binding poses; fourthly, we established a leaderboard to rank submitted models in real-time. We derived some key insights and conclusions from extensive experiments: (1) AI-based approaches have consistently outperformed physics-based methods in overall docking success rate. (2) Most intra- and intermolecular clashes of AI-based approaches can be greatly alleviated with relaxation, which means combining AI modeling with physics-based post-processing could achieve excellent performance. (3) AI co-folding methods commonly exhibit ligand chirality issues, except for Boltz-1x, which introduced physics-inspired potentials to fix hallucinations, suggesting the modeling on stereochemistry improves the structural plausibility markedly. (4) Specifying binding pockets significantly promotes docking performance, indicating that pocket information can be leveraged adequately, particularly for AI co-folding methods, in future modeling efforts. The code, dataset, and leaderboard are released at https://github.com/CataAI/PoseX.

Dataset URL: https://huggingface.co/datasets/CataAI/PoseX

Code URL: https://github.com/CataAI/PoseX

Primary Area: AL/ML Datasets & Benchmarks for life sciences (e.g. climate, health, life sciences, physics, social sciences)

Submission Number: 933