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

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arxiv 2310.05764 v4 pith:5AEB2F2C submitted 2023-10-09 cs.LG cs.AI

classification cs.LGcs.AI
keywords bindingdockingflowflowsitegenerativeharmonicflowmatchingmolecules
verification ladder T0 review T1 audit T2 compute T3 formal
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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.

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Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Feynman-Kac-Flow: Inference Steering of Conditional Flow Matching to an Energy-Tilted Posterior

    cs.LG 2025-09 conditional novelty 6.0 of 10

    Feynman-Kac particle steering, previously diffusion-only, is derived for conditional flow matching and used to generate chirality-correct chemical transition states.

  2. Applications of Modular Co-Design for De Novo 3D Molecule Generation

    cs.LG 2025-05 conditional novelty 6.0 of 10

    A new transformer architecture with joint continuous and discrete denoising improves 3D molecule generation and moves generated structures closer to low-energy physical minima.

  3. Energy-Based Flow Matching for Generating 3D Molecular Structure

    cs.LG 2025-08 conditional novelty 5.0 of 10

    IDFlow trains a flow matching network to refine its own predicted 3D molecular structure, improving docking and protein backbone generation over HarmonicFlow and FrameFlow baselines.

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