{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Lee J"],"funding":["Ministry of Health and Welfare","Ministry of Science and ICT, South Korea"],"pagination":["e02702"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC12463045"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["12(35)"],"pubmed_abstract":["Recent remarkable advancements in geometric deep generative models, coupled with accumulated structural data, enable structure-based drug design (SBDD) using only target protein information. However, existing models often struggle to balance multiple objectives, excelling only in specific tasks. BInD, a diffusion model with knowledge-based guidance, is introduced to address this limitation by co-generating molecules and their interactions with a target protein. This approach ensures balanced consideration of key objectives, including target-specific interactions, molecular properties, and local geometry. Comprehensive evaluations demonstrate that BInD achieves robust performance across all objectives, matching or surpassing state-of-the-art methods. Additionally, an NCI-driven molecule design and optimization method is proposed, enabling the enhancement of target binding and specificity by elaborating the adequate interaction patterns."],"journal":["Advanced science (Weinheim, Baden-Wurttemberg, Germany)"],"pubmed_title":["BInD: Bond and Interaction-Generating Diffusion Model for Multi-Objective Structure-Based Drug Design."],"pmcid":["PMC12463045"],"funding_grant_id":["RS‐2024‐00512498","NRF-2022M3J6A1063021","RS-2023-NR077040","RS‐2023‐00257479","RS-2024-00512498","RS‐2023‐NR077040","RS-2023-00257479","NRF‐2022M3J6A1063021"],"pubmed_authors":["Seo J","Kim WY","Lee J","Zhung W"],"additional_accession":[]},"is_claimable":false,"name":"BInD: Bond and Interaction-Generating Diffusion Model for Multi-Objective Structure-Based Drug Design.","description":"Recent remarkable advancements in geometric deep generative models, coupled with accumulated structural data, enable structure-based drug design (SBDD) using only target protein information. However, existing models often struggle to balance multiple objectives, excelling only in specific tasks. BInD, a diffusion model with knowledge-based guidance, is introduced to address this limitation by co-generating molecules and their interactions with a target protein. This approach ensures balanced consideration of key objectives, including target-specific interactions, molecular properties, and local geometry. Comprehensive evaluations demonstrate that BInD achieves robust performance across all objectives, matching or surpassing state-of-the-art methods. Additionally, an NCI-driven molecule design and optimization method is proposed, enabling the enhancement of target binding and specificity by elaborating the adequate interaction patterns.","dates":{"release":"2025-01-01T00:00:00Z","publication":"2025 Sep","modification":"2026-06-03T21:16:00.729Z","creation":"2026-05-01T03:11:07.244Z"},"accession":"S-EPMC12463045","cross_references":{"pubmed":["40642896"],"doi":["10.1002/advs.202502702"]}}