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

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

Keywords: Molecular Docking, Flow Matching, Riemannian Flow Matching, Protein-Ligand Interaction

Abstract:

Accurate prediction of protein-ligand binding poses is crucial for structure-based drug design, yet existing methods struggle to balance speed, accuracy, and physical plausibility.
We introduce Matcha, a novel molecular docking pipeline that combines multi-stage flow matching with learned scoring and physical validity filtering.
Our approach consists of three sequential stages applied consecutively to progressively refine docking predictions,
each implemented as a flow matching model operating on appropriate geometric spaces ($\mathbb{R}^3$, $\mathrm{SO}(3)$, and $\mathrm{SO}(2)$).
We enhance the prediction quality through a dedicated scoring model and apply unsupervised physical validity filters to eliminate unrealistic poses.
Compared to various approaches, Matcha demonstrates superior performance on Astex and PDBbind test sets in terms of docking success rate and physical plausibility.
Moreover, our method works approximately $25 \times$ faster than modern large-scale co-folding models.

Primary Area: applications to physical sciences (physics, chemistry, biology, etc.)

Submission Number: 18526