PocketVina Enables Scalable and Highly Accurate Physically Valid Docking through Multi-Pocket Conditioning
This addresses the problem of scalable and accurate molecular docking for drug discovery researchers, offering a robust method that is efficient for high-throughput virtual screening.
The researchers tackled the challenge of sampling physically valid ligand-binding poses in molecular docking by introducing PocketVina, a search-based framework that combines pocket prediction with multi-pocket exploration. The result was state-of-the-art performance in physically valid docking across multiple benchmarks, with competitive accuracy on unseen targets and successful discrimination of active from inactive targets on a large-scale dataset.
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.