{"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/ncidiff-non-covalent-interaction-generative","title":"BInD: Bond and Interaction-generating Diffusion Model for Multi-objective Structure-based Drug Design","arxiv_id":"2405.16861","date":"2024-05-27","proceeding":null,"authors":["Joongwon Lee","Wonho Zhung","Jisu Seo","Woo Youn Kim"],"abstract":"A remarkable advance in geometric deep generative models with accumulated structural data enables structure-based drug design (SBDD) with target protein information only. However, most existing models struggle to address multi-objectives simultaneously while performing well only in their specialized tasks. Here, we present BInD, a diffusion model with knowledge-based guidance for multi-objective SBDD. BInD is designed to co-generate molecules and their interactions with a target protein to consider all key objectives equally well, including target-specific interactions, molecular properties, and local geometry. Comprehensive evaluations show that BInD achieves robust performance for all objectives while outperforming or matching state-of-the-art methods for each. Finally, we propose a train-free optimization method empowered by retrieving target-specific interactions, highlighting the role of non-covalent interactions in achieving higher selectivity and binding affinities to a target protein.","url_abs":"https://arxiv.org/abs/2405.16861v2","url_pdf":"https://arxiv.org/pdf/2405.16861v2.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":"ncidiff-non-covalent-interaction-generative","repo_url":"https://github.com/lee-jwon/bind","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"3d-molecule-generation","task_name":"3D Molecule Generation"},{"task_slug":"drug-design","task_name":"Drug Design"},{"task_slug":"drug-discovery","task_name":"Drug Discovery"}],"methods":[{"method_slug":"diffusion","method_name":"Diffusion"},{"method_slug":"pixel-prediction","method_name":"Inpainting"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}