bio-virtual-screening
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 poses, or selecting a docking workflow for a specific scenario.
npx skills add BioTender-max/awesome-bio-agent-skills --skill virtual-screening --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
## Version Compatibility Reference examples tested with: AutoDock Vina 1.2.5+, SMINA 2020-12+, GNINA 1.1+, 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 fails physical-plausibility tests (PoseBusters) ~50% of the time; the postdoc-grade workflow combines ML pose sampling with classical scoring and physical validation. For ultralarge libraries (>1M), library preparation, hierarchical filtering, and HPC orchestration become the limit
- Version Compatibility
- Docking Tool Taxonomy
- Decision Tree by Scenario
- Receptor Preparation
- Ligand Preparation
- Binding Site Detection
- Vina Docking (Single Ligand)
- GNINA with CNN Scoring (modern default)
- Virtual Screening Pipeline (Hierarchical)
- Ultralarge Library Screening (ZINC22, Enamine REAL)
- Per-Tool Failure Modes
- Vina -- cross-dock failure
- GNINA CNN -- novel chemotype out-of-distribution
- Box too small
prank predict -f receptor.pdb -o pockets/ gnina -r receptor.pdb -l ligand.sdf \
What does the bio-virtual-screening skill do?
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 poses, or selecting a docking workflow for a specific scenario.
How do I install it?
Run `npx skills add BioTender-max/awesome-bio-agent-skills --skill virtual-screening --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From BioTender-max/awesome-bio-agent-skills, a repository with 135 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.