@inproceedings{
stark2023harmonic,
title={Harmonic Prior Self-conditioned Flow Matching for Multi-Ligand Docking and Binding Site Design},
author={Hannes Stark and Bowen Jing and Regina Barzilay and Tommi Jaakkola},
booktitle={NeurIPS 2023 AI for Science Workshop},
year={2023},
url={https://openreview.net/forum?id=3WF88uMjGz}
}
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.
Harmonic Prior Self-conditioned Flow Matching for Multi-Ligand Docking and Binding Site Design
Hannes Stark Bowen Jing Regina Barzilay Tommi Jaakkola