{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/accurate-pocket-identification-for-binding","title":"Accurate Pocket Identification for Binding-Site-Agnostic Docking","arxiv_id":"2502.02371","date":"2025-02-04","proceeding":null,"authors":["Yaroslav Balytskyi","Inna Hubenko","Alina Balytska","Christopher V. Kelly"],"abstract":"Accurate identification of druggable pockets is essential for structure-based drug design. However, most pocket-identification algorithms prioritize their geometric properties over downstream docking performance. To address this limitation, we developed RAPID-Net, a pocket-finding algorithm for seamless integration with docking workflows. When guiding AutoDock Vina, RAPID-Net outperforms DiffBindFR on the PoseBusters benchmark and enables blind docking on large proteins that AlphaFold 3 cannot process as a whole. Furthermore, RAPID-Net surpasses PUResNet and Kalasanty in docking accuracy and pocket-ligand intersection rates across diverse datasets, including PoseBusters, Astex Diverse Set, BU48, and Coach420. When accuracy is evaluated as ``at least one correct pose in the ensemble'', RAPID-Net outperforms AlphaFold 3 on the PoseBusters benchmark, suggesting that our approach can be further improved with a suitable pose reweighting tool offering a cost-effective and competitive alternative to AlphaFold 3 for docking. Finally, using several therapeutically relevant examples, we demonstrate the ability of RAPID-Net to identify remote functional sites, highlighting its potential to facilitate the development of innovative therapeutics.","url_abs":"https://arxiv.org/abs/2502.02371v1","url_pdf":"https://arxiv.org/pdf/2502.02371v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"accurate-pocket-identification-for-binding","repo_url":"https://github.com/balytskyijaroslaw/rapid-net","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"tf","reach":null}],"tasks":[{"task_slug":"blind-docking","task_name":"Blind Docking"},{"task_slug":"drug-design","task_name":"Drug Design"}],"methods":[{"method_slug":"alphafold","method_name":"AlphaFold"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}