FlexSBDD: Structure-Based Drug Design with Flexible Protein Modeling

TL;DR

FlexSBDD is a novel deep generative model that accurately captures protein flexibility during ligand design, significantly improving the quality and biological relevance of generated molecules in structure-based drug discovery.

Contribution

It introduces a flow matching framework with E(3)-equivariant networks and data augmentation techniques to model protein flexibility, addressing limitations of rigid protein assumptions in SBDD.

Findings

Achieves state-of-the-art performance in high-affinity molecule generation

Effectively models protein conformational changes to enhance interactions

Reduces steric clashes in generated ligand structures

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.