bio-virtual-screening skillA
bio-virtual-screening is agent-read markdown (skill) from pku-yuangroup/openai4s: Performs structure-based virtual screening using AutoDock Vina, SMINA, GNINA (CNN scoring), and DiffDock-L hybrid workflows with explicit choice rules across rigid vs flexible docking, cross-docking vs self-docking, binding-site detection (P2Rank, fpocket), receptor preparation (PDB2PQR, PROPKA), ligand preparation (meeko, OpenBabel), and ultralarge-library screening (ZINC22, Enamine REAL). Use when screening chemical libraries against a protein target to find candidate binders, ranking docking .
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: AutoDock Vina 1.2.5+, SMINA 2020-12+, GNINA 1.1+ for `rescore` (GNINA 1.3+ for the six-mode interface documented below), RDKit 2024.09+, meeko 0.5+, P2Rank 2.4+, ProDy 2.4+, pdb2pqr 3.6+. Before using code patterns, verify installed versions match. If versions differ: - Python: `pip show <package>` then `help(module.function)` to check signatures - CLI: `vina --version`; `gnina --version`; `smina --version` If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying. # Virtual Screening Screen chemical libraries against protein targets via molecular docking. Vina is the de-facto default, SMINA adds flexibility (Vinardo scoring, custom scoring), and GNINA adds CNN-based pose scoring (Top-1 redock 58%->73% over Vina, cross-dock 27%->37%). Deep-learning docking (DiffDock-L, EquiBind, NeuralPLexer) competes in pose accuracy, but physical validity is method- and dataset-dependent; the workflow therefore combines ML pose sampling with classical scoring and explicit geometry checks. For ultralarge libraries (>1M), library… …
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How to install
mdr add pku-yuangroup/openai4s/bio-virtual-screening@git:20260821.2d1b678mdr add pku-yuangroup/openai4s/bio-virtual-screening@sha256:6976ad18d658f237Pin 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_k3ax7zmtywlptouq)
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Audit of the latest version
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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_k3ax7zmtywlptouq GET https://markdownregistry.com/api/v1/resolve?ref=pku-yuangroup/openai4s/bio-virtual-screening GET https://markdownregistry.com/api/v1/blob/6976ad18d658f237b32c602147636a4d5f20d28f29cdb542aa71fefdc61930e5
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