{"ID":2879026,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2508.17555","arxiv_id":"2508.17555","title":"Boltzina: Efficient and Accurate Virtual Screening via Docking-Guided Binding Prediction with Boltz-2","abstract":"In structure-based drug discovery, virtual screening using conventional molecular docking methods can be performed rapidly but suffers from limitations in prediction accuracy. Recently, Boltz-2 was proposed, achieving extremely high accuracy in binding affinity prediction, but requiring approximately 20 seconds per compound per GPU, making it difficult to apply to large-scale screening of hundreds of thousands to millions of compounds. This study proposes Boltzina, a novel framework that leverages Boltz-2's high accuracy while significantly improving computational efficiency. Boltzina achieves both accuracy and speed by omitting the rate-limiting structure prediction from Boltz-2's architecture and directly predicting affinity from AutoDock Vina docking poses. We evaluate on eight assays from the MF-PCBA dataset and show that while Boltzina performs below Boltz-2, it provides significantly higher screening performance compared to AutoDock Vina and GNINA. Additionally, Boltzina achieved up to 11.8$\\times$ faster through reduced recycling iterations and batch processing. Furthermore, we investigated multi-pose selection strategies and two-stage screening combining Boltzina and Boltz-2, presenting optimization methods for accuracy and efficiency according to application requirements. This study represents the first attempt to apply Boltz-2's high-accuracy predictions to practical-scale screening, offering a pipeline that combines both accuracy and efficiency in computational biology. The Boltzina is available on github; https://github.com/ohuelab/boltzina.","short_abstract":"In structure-based drug discovery, virtual screening using conventional molecular docking methods can be performed rapidly but suffers from limitations in prediction accuracy. Recently, Boltz-2 was proposed, achieving extremely high accuracy in binding affinity prediction, but requiring approximately 20 seconds per com...","url_abs":"https://arxiv.org/abs/2508.17555","url_pdf":"https://arxiv.org/pdf/2508.17555v1","authors":"[\"Kairi Furui\",\"Masahito Ohue\"]","published":"2025-08-24T23:40:10Z","proceeding":"q-bio.BM","tasks":"[\"q-bio.BM\",\"cs.CE\",\"cs.LG\",\"q-bio.QM\"]","methods":"[]","has_code":false,"code_links":[{"ID":610542,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_id":2879026,"paper_url":"https://arxiv.org/abs/2508.17555","paper_title":"Boltzina: Efficient and Accurate Virtual Screening via Docking-Guided Binding Prediction with Boltz-2","repo_url":"https://github.com/ohuelab/boltzina","is_official":false,"mentioned_in_paper":false,"mentioned_in_github":true,"github_stars":0}]}