posebusters

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.

Reproducible predictors for time-dependent CYP3A4 and CYP2D6 inhibition and CYP3A4-ligand structures (OpenADMET CYP Inhibition Blind Challenge): nested cross-validation without selection bias, pre-registered gates, a ledger of negative results and an audited rebuild.

Unofficial MCP server for PoseBusters – validate molecular poses via HTTP or Spaces using the Model Context Protocol (MCP).

Benchmarking DiffDock-Pocket on LP-HiQBind and PoseBusters: ~50% of poses within 2 Å, but under 7% physically valid. Full HTCondor training, inference and evaluation pipeline.

Standalone C++26 PoseBusters-compatible pose validation (NativePoseQC + optional upstream bust bridge). Apache-2.0. Independent of FlexAIDdS.

ML rescoring of AutoDock Vina poses on the PoseBusters set (RDKit, ProLIF, PyTorch) with a leakage audit: honest negative result

Docking by denoising: DiffDock-L candidate poses, their reverse-diffusion trajectories and physical checks, and a frozen pilot comparing four ways to select a pose. Interactive article.

A measured benchmark of three docking scoring functions on 308 protein-ligand complexes, reporting pose accuracy and physical validity together, with the success rate recomputed after removing the crystal starting conformer and the ligand-centred search box.