2024/05/27 by Lee, Joongwon, Zhung, Wonho, Seo, Jisu +1 · 1 citation
#Biological Physics (physics.bio-ph) #Biomolecules (q-bio.BM) #FOS: Biological sciences #FOS: Computer and information sciences #FOS: Physical sciences #Machine Learning (cs.LG)
paper · doi:10.48550/arxiv.2405.16861
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.