Harmonic Prior Self-conditioned Flow Matching for Multi-Ligand Docking and Binding Site Design

Published: 28 Oct 2023, Last Modified: 28 Oct 2023NeurIPS2023-AI4Science OralEveryoneRevisionsBibTeX
Keywords: proteins, flow matching, flow model, generative model, docking
TL;DR: self-conditioned flow to generate binding structures and an extension to discrete residue types for joint binding site design
Abstract: A significant amount of protein function requires binding small molecules, including enzymatic catalysis. As such, designing binding pockets for small molecules has several impactful applications ranging from drug synthesis to energy storage. Towards this goal, we first develop HarmonicFlow, an improved generative process over 3D protein-ligand binding structures based on our self-conditioned flow matching objective. FlowSite extends this flow model to jointly generate a protein pocket's discrete residue types and the molecule's binding 3D structure. We show that HarmonicFlow improves upon the state-of-the-art generative processes for docking in simplicity, generality, and performance. Enabled by this structure model, FlowSite designs binding sites substantially better than baseline approaches and provides the first general solution for binding site design.
Submission Track: Original Research
Submission Number: 133