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bio-pose-validation skillA

bio-pose-validation is agent-read markdown (skill) from pku-yuangroup/openai4s: Validates docked / generated protein-ligand poses using PoseBusters physical-validity tests, strain energy quantification, geometric checks (planarity, vdW overlap, bond/angle distortion), and pose-energy reasonableness. Use when QC-ing docking results, comparing classical vs ML docking outputs, or filtering pose lists before SAR analysis..

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## Version Compatibility

Reference examples tested with: PoseBusters 0.6+, RDKit 2024.09+, pandas 2.2+, posecheck 0.5+ (optional).

Before using code patterns, verify installed versions match. If versions differ:
- Python: `pip show <package>` then `help(module.function)` to check signatures

If code throws ImportError, AttributeError, or TypeError, introspect the installed
package and adapt the example to match the actual API rather than retrying.

# Pose Validation

Test docked or AI-generated protein-ligand poses for physical plausibility. PoseBusters (Buttenschoen et al. 2024) provides geometric, chemical, and energetic checks that flag implausible poses, including non-planar aromatic rings, van der Waals clashes, broken bonds, altered stereochemistry, and unfavorable internal energies. On the Astex Diverse Set, DiffDock achieved 72% RMSD success but only 47% combined RMSD-and-PB-valid success; the size of this gap is dataset- and method-dependent. PB-valid status complements RMSD for downstream SAR, FEP setup, or generative-model training.

For docking, see `chemoinformatics/virtual-screening`. For ML docking specifically, see `chemoinformatics/ml-docking-rescoring`.
…

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Source

GitHub

pku-yuangroup/openai4s · 586 stars · license MIT · pushed 2026-09-23 · branch main

API

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