PocketVina Enables Scalable and Highly Accurate Physically Valid Docking through Multi-Pocket Conditioning

Ahmet Sarigun; Bora Uyar; Vedran Franke; Altuna Akalin

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arXiv · 2506.20043

PocketVina Enables Scalable and Highly Accurate Physically Valid Docking through Multi-Pocket Conditioning

Ahmet Sarigun·Bora Uyar·Vedran Franke·Altuna Akalin

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Abstract

Sampling physically valid ligand-binding poses remains a major challenge in molecular docking, particularly for unseen or structurally diverse targets. We introduce PocketVina, a fast and memory-efficient, search-based docking framework that combines pocket prediction with systematic multi-pocket exploration. We evaluate PocketVina across four established benchmarks--PDBbind2020 (timesplit and unseen), DockGen, Astex, and PoseBusters--and observe consistently strong performance in sampling physically valid docking poses. PocketVina achieves state-of-the-art performance when jointly considering ligand RMSD and physical validity (PB-valid), while remaining competitive with deep learning-based approaches in terms of RMSD alone, particularly on structurally diverse and previously unseen targets. PocketVina also maintains state-of-the-art physically valid docking accuracy across ligands with varying degrees of flexibility. We further introduce TargetDock-AI, a benchmarking dataset we curated, consisting of over 500000 protein-ligand pairs, and a partition of the dataset labeled with PubChem activity annotations. On this large-scale dataset, PocketVina successfully discriminates active from inactive targets, outperforming a deep learning baseline while requiring significantly less GPU memory and runtime. PocketVina offers a robust and scalable docking strategy that requires no task-specific training and runs efficiently on standard GPUs, making it well-suited for high-throughput virtual screening and structure-based drug discovery.

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Ahmet Sarigun, Bora Uyar, Vedran Franke, Altuna Akalin. 2025-06-24. PocketVina Enables Scalable and Highly Accurate Physically Valid Docking through Multi-Pocket Conditioning. https://arxiv.org/abs/2506.20043

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