English

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

Machine Learning 2024-06-03 v4 Artificial Intelligence

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 state-of-the-art generative processes for docking in simplicity, generality, and average sample quality in pocket-level docking. Enabled by this structure modeling, FlowSite designs binding sites substantially better than baseline approaches.

Keywords

Cite

@article{arxiv.2310.05764,
  title  = {Harmonic Self-Conditioned Flow Matching for Multi-Ligand Docking and Binding Site Design},
  author = {Hannes Stärk and Bowen Jing and Regina Barzilay and Tommi Jaakkola},
  journal= {arXiv preprint arXiv:2310.05764},
  year   = {2024}
}

Comments

Published at ICML 2024. (Proceedings of the 41st International Conference on Machine Learning, Vienna, Austria. PMLR 235, 2024)

R2 v1 2026-06-28T12:44:43.321Z