bio-ml-docking-rescoring skillA
bio-ml-docking-rescoring is agent-read markdown (skill) from pku-yuangroup/openai4s: Performs ML-based protein-ligand pose prediction and scoring using DiffDock-L (diffusion-based), Boltz-1 / Boltz-2 (foundation model with affinity), Chai-1, AlphaFold3 ligand, EquiBind, TANKBind, NeuralPLexer, and hybrid workflows (DiffDock pose + GNINA rescore + PoseBusters QC). Explicit handling of when ML beats classical docking, when classical beats ML, the PB-invalid pose problem, and rescoring as the standard production hybrid. Use when modern docking is needed: foundation-model ligand-pos.
Indexed from public GitHub and served as immutable, content-addressed versions. Install it pinned to an exact SHA-256 with the mdr CLI, and every file is verified against the hash recorded here before it reaches your agent. The deterministic audit below grades the latest version, and the same file always earns the same grade.
What the file says
## Version Compatibility Reference examples tested with: DiffDock-L (Corso et al. 2024), Boltz-1 1.0+, Boltz-2 (Passaro et al. 2025), Chai-1 0.4+, AlphaFold 3 (DeepMind), EquiBind, TANKBind, GNINA 1.1+, and PoseBusters 0.6+. Before using code patterns, verify installed versions match. If versions differ: - Python: `pip show <package>` then `help(module.function)` to check signatures - CLI: `diffdock --version`; `boltz --version` If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying. # ML Docking and Rescoring Use machine-learning models for protein-ligand pose prediction and affinity scoring. Foundation models such as AlphaFold 3, Boltz, and Chai-1 handle protein-ligand complex prediction, while DiffDock-L extends the original DiffDock method for ligand-pose sampling (Corso et al. 2023, 2024). Boltz-2 reports affinity prediction approaching physics-based free-energy methods on its evaluated benchmarks at substantially lower computational cost. Physical plausibility remains a separate requirement: on the PoseBusters Benchmark, the original DiffDock produced a correct and… …
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How to install
mdr add pku-yuangroup/openai4s/bio-ml-docking-rescoring@git:20260821.2d1b678mdr add pku-yuangroup/openai4s/bio-ml-docking-rescoring@sha256:79905b35c77f5a82Pin to a label to follow the author's releases, or to a sha256 to freeze the exact bytes forever. Either way the resolved hash is written to mdr.lock, and mdr install reproduces it on any machine.
[](https://markdownregistry.com/a/art_jqo3z7fta263vvrj)
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Source
pku-yuangroup/openai4s · 586 stars · license MIT · pushed 2026-09-23 · branch main
API
GET https://markdownregistry.com/api/v1/artifacts/art_jqo3z7fta263vvrj GET https://markdownregistry.com/api/v1/resolve?ref=pku-yuangroup/openai4s/bio-ml-docking-rescoring GET https://markdownregistry.com/api/v1/blob/79905b35c77f5a820813ecc7d7ad5acff944e0c534ce73e979a47db42c0e22aa
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