Multi-stage Riemannian flow matching for physically valid molecular docking, with GNINA scoring, PoseBusters filtering, CLI inference, and benchmarks.
-
Updated
May 14, 2026 - Python
Multi-stage Riemannian flow matching for physically valid molecular docking, with GNINA scoring, PoseBusters filtering, CLI inference, and benchmarks.
High-throughput docking pose validation: symmetry-corrected RMSD and lightweight PoseBusters-style distance/clash filters.
Unofficial MCP server for PoseBusters – validate molecular poses via HTTP or Spaces using the Model Context Protocol (MCP).
ML rescoring of AutoDock Vina poses on the PoseBusters set (RDKit, ProLIF, PyTorch) with a leakage audit: honest negative result
To associate your repository with the posebusters topic, visit your repo's landing page and select "manage topics."