FlexSBDD: Structure-Based Drug Design with Flexible Protein Modeling
Keywords: Structure-based drug design, Generative models, AI for Science, Protein Modeling, Steric Clashes, Protein-ligand Interactions
TL;DR: FlexSBDD is a deep generative model capable of accurately modeling the flexible target protein structure for ligand molecule generation.
Abstract: Structure-based drug design (SBDD), which aims to generate 3D ligand molecules binding to target proteins, is a fundamental task in drug discovery. Existing SBDD methods typically treat protein as rigid and neglect protein structural change when binding with ligand molecules, leading to a big gap with real-world scenarios and inferior generation qualities (e.g., many steric clashes). To bridge the gap, we propose FlexSBDD, a deep generative model capable of accurately modeling the flexible protein-ligand complex structure for ligand molecule generation. FlexSBDD adopts an efficient flow matching framework and leverages E(3)-equivariant network with scalar-vector dual representation to model dynamic structural changes. Moreover, novel data augmentation schemes based on structure relaxation/sidechain repacking are adopted to boost performance. Extensive experiments demonstrate that FlexSBDD achieves state-of-the-art performance in generating high-affinity molecules and effectively modeling the protein's conformation change to increase favorable protein-ligand interactions (e.g., Hydrogen bonds) and decrease steric clashes.
Primary Area: Machine learning for other sciences and fields
Submission Number: 1360
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