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BInD: Bond and Interaction-generating Diffusion Model for Multi-objective Structure-based Drug Design (2405.16861v2)

Published 27 May 2024 in q-bio.BM, cs.LG, and physics.bio-ph

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.

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