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PoseX: AI Defeats Physics Approaches on Protein-Ligand Cr...
Yize Jiang, · 2026-04-28 · via cs.LG updates on arXiv.org

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Abstract:Existing protein-ligand docking studies typically focus on the self-docking scenario, which is less practical in real applications. Moreover, some studies involve heavy frameworks requiring extensive training, posing challenges for convenient and efficient assessment of docking methods. To fill these gaps, we design PoseX, an open-source benchmark 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; second, 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); third, we developed a relaxation method for post-processing to minimize conformational energy and refine binding poses; fourth, we built a leaderboard to rank submitted models in real-time. We derived some key insights and conclusions from extensive experiments: (1) AI approaches have consistently outperformed physics-based methods in overall docking success rate. (2) Most intra- and intermolecular clashes of AI 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 exhibit ligand chirality issues, except for Boltz-1x, which introduced physics-inspired potentials to fix hallucinations, suggesting 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 this https URL.
Subjects: Machine Learning (cs.LG); Quantitative Methods (q-bio.QM)
Cite as: arXiv:2505.01700 [cs.LG]
  (or arXiv:2505.01700v3 [cs.LG] for this version)
  https://doi.org/10.48550/arXiv.2505.01700

arXiv-issued DOI via DataCite

Submission history

From: Tianfan Fu [view email]
[v1] Sat, 3 May 2025 05:35:37 UTC (4,810 KB)
[v2] Wed, 21 May 2025 06:15:35 UTC (5,949 KB)
[v3] Sun, 26 Apr 2026 04:52:32 UTC (4,802 KB)

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