PocketVina enables scalable and highly accurate physically valid docking through multi-pocket conditioning

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Item Type:	Preprint
Title:	PocketVina enables scalable and highly accurate physically valid docking through multi-pocket conditioning
Creators Name:	Sarigun, A., Uyar, B., Franke, V. and Akalin, A.
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.
Keywords:	Molecular Docking, Search-Based Algorithm, Multi-Pocket Conditioned Docking
Source:	arXiv
Publisher:	Cornell University
Article Number:	2506.20043
Date:	24 June 2025
Official Publication:	https://doi.org/10.48550/arXiv.2506.20043

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