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arXiv Machine Learning By Daria Frolova, Talgat Daulbaev, Egor Sevriugov, Sergei A. Nikolenko, Dmitry N. Ivankov, Ivan Oseledets, Marina A. Pak

Matcha: Multi-Stage Riemannian Flow Matching for Accurate and Physically Valid Molecular Docking

Read the original on arXiv Machine Learning →

Matcha is a new molecular docking method that refines ligand poses through three flow-matching stages—translation, rotation, and torsion—before applying GNINA for minimization, filtering, and ranking. It achieves high physical plausibility and is approximately 28 times faster than current large-scale co‑folding models, outperforming existing benchmarks. The authors provide code, benchmarking artifacts, and model checkpoints on GitHub and Zenodo.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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