← 研踪 PaperTrailarXiv:2510.14586 · v4
Matcha: Multi-Stage Riemannian Flow Matching for Accurate and Physically Valid Molecular Docking
首次提交 2025-10-16 19:44
版本更新 2026-10-26 21:10
雷达观测 2026-10-29 00:36
摘要
Predicting a protein--ligand binding pose is central to structure-based drug design, yet current docking methods still struggle to combine speed, accuracy and physical plausibility. We introduce Matcha, which refines a ligand pose by three flow-matching stages in translation, rotation and torsion space, then minimizes, filters and ranks candidates with GNINA. Across the benchmarks considered, Matcha attains the strongest physical plausibility and runs about $28\times$ faster than modern large-scale co-folding models. Code and benchmarking artifacts are available at https://github.com/LigandPro/Matcha and archived at https://doi.org/10.5281/zenodo.23568093. Model checkpoints are archived at https://doi.org/10.5281/zenodo.23569055.
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v4 · 2026-10-26 21:10 · 最新观测版本
Matcha: Multi-Stage Riemannian Flow Matching for Accurate and Physically Valid Molecular Docking
Predicting a protein--ligand binding pose is central to structure-based drug design, yet current docking methods still struggle to combine speed, accuracy and physical plausibility. We introduce Matcha, which refines a ligand pose by three flow-matching stages in translation, rotation and torsion space, then minimizes, filters and ranks candidates with GNINA. Across the benchmarks considered, Matcha attains the strongest physical plausibility and runs about $28\times$ faster than modern large-scale co-folding models. Code and benchmarking artifacts are available at https://github.com/LigandPro/Matcha and archived at https://doi.org/10.5281/zenodo.23568093. Model checkpoints are archived at https://doi.org/10.5281/zenodo.23569055.
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