Virtual Screening

What the source does

• FilesystemNone observedNo reads or writes in the source
• NetworkNone observedNo outbound calls in the source
• ShellNone observedNo commands run in the source
• DependenciesPinnedNothing floats
• SecretsNone foundNo keys in source

Five audit layers

Gates applied: no behavioural pass.

Audit 0.4.1 · source sha c8c1fc3a2b13 · 19 Oct 2026Report an issue or request a re-scan

• PublisherGPTomics GitHub organizationOwner of the repository this skill is read from
• Source folderGPTomics/bioSkills/chemoinformatics/virtual-screeningThe exact folder we audited
• LicenceMITAs the repository states it
• Commit pinned for this verdictc8c1fc3a2b13A new audit runs when the source changes
• Repository statusArchivedLast push 65 days ago
• Sandboxed runNot runSkills are read, not executed; layer L4 is skipped

Its description: 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,…

---
name: bio-virtual-screening
description: 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.
tool_type: python
primary_tool: AutoDock Vina
---

## 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 preparation, hierarchical filtering, and HPC orchestration become the limiting steps.

For pose physical-validity QC, see `chemoinformatics/pose-validation`. For ML-driven docking + rescoring, see `chemoinformatics/ml-docking-rescoring`. For covalent docking, see `chemoinformatics/covalent-design`. For affinity calculations (FEP), see `chemoinformatics/free-energy-calculations`.

## Docking Tool Taxonomy

| Tool | Scoring | Speed (sec/lig) | Best at | Fails when |
|------|---------|-----------------|---------|------------|
| AutoDock Vina 1.2 | Vina (empirical) | Hardware- and settings-dependent | Open, well-characterized baseline | Cross-dock; cryptic pockets; metal centers |
| SMINA | Vina + flexible + custom | Hardware- and settings-dependent | Custom scoring; flexible side chains | Same Vina-scoring caveats |
| Vinardo | Modified Vina scoring | Hardware- and settings-dependent | Alternative empirical score | Validate on target-relevant controls |
| GNINA 1.1 | CNN or Vina scoring | GPU- and settings-dependent | CNN-assisted pose ranking | Validate transfer to the target and chemotype |
| AutoDock 4 | AD4 + grid maps | Hardware- and settings-dependent | Legacy reference | More setup than Vina |
| DOCK 6/7 | DOCK + Amber | Hardware- and settings-dependent | UCSF DOCK ecosystem | Steep learning curve |
| Glide (Schrodinger) | GlideScore | License and hardware-dependent | Commercial docking workflow | License cost |
| GOLD (CCDC) | GOLDScore / ChemScore | License and hardware-dependent | Commercial workflow; metal options | License cost |
| FlexX (BioSolveIT) | FlexX | License and hardware-dependent | Fragment-based placement | License cost |
| rDock | rDock | Hardware- and settings-dependent | Open-source alternative | Validate maintenance and target fit |
| DiffDock-L | Diffusion-generative | GPU- and settings-dependent | Pose sampling for cross-docking | Validate geometry with PoseBusters; see ml-docking-rescoring |
| EquiBind | Equivariant NN | GPU- and settings-dependent | Single-shot pose generation | Requires independent geometry and ranking checks |
| Boltz-2 + GNINA rescore | Foundation model + CNN | GPU- and settings-dependent | Experimental multi-model workflow | Benchmark each evidence stream independently |

**Decision:** Use Vina as an open baseline and consider GNINA CNN rescoring when target-relevant redocking or cross-docking controls support it. For large libraries, calibrate a hierarchical Vina -> GNINA -> higher-cost follow-up workflow on measured enrichment, throughput, and retained chemotype diversity.

## Decision Tree by Scenario

| Scenario | Recommended workflow |
|----------|---------------------|
| Self-dock against known ligand pocket | GNINA `gnina --cnn_scoring rescore` |
| Cross-dock to apo or related-target structure | DiffDock-L pose + GNINA rescore + PoseBusters |
| Ultralarge library (10M+) | Calibrated hierarchical screen: property/alert triage -> Vina -> measured top fraction to GNINA -> higher-cost follow-up |
| Cryptic pocket / induced fit | Receptor-ensemble docking and, where appropriate, a separately validated complex-prediction model |
| Allosteric / undefined site | P2Rank for pocket detection -> ensemble dock all pockets |
| Metal-coordinated ligand | GOLD (commercial) or manually parameterize Vina metal scoring |
| Covalent inhibitor | See `chemoinformatics/covalent-design`: DOCKovalent, HCovDock |
| Fragment screen (<300 Da) | rDock or constrained Vina with seed atoms |
| Hit-to-lead refinement | Use co-crystal structure if available; MD-relaxed receptor; FEP for affinity |

## Receptor Preparation

**Goal:** Convert a protein PDB into a docking-ready format with correct protonation, missing atoms, and removed waters.

**Approach:** Decide which ligands, cofactors, metals, and structural waters to retain -> fill missing heavy atoms with a structure-repair tool such as PDBFixer -> use PROPKA/PDB2PQR plus manual review to assign pH-dependent protonation -> assign the charge model required by the docking workflow -> prepare receptor PDBQT with a documented AutoDock-compatible tool.

```python
import subprocess
from pathlib impor

Excerpt, first 6,000 characters of SKILL.md, verbatim from the audited commit. It is text from someone else’s repository, quoted so you can judge it before your agent reads it.

pip install vina
pip install rdkit meeko

git clone https://github.com/GPTomics/bioSkills
git -C bioSkills checkout c8c1fc3a2b13

Works in

1. 2026-10-19
   ✓SAFE B 89 / 100

   first audit · source c8c1fc3a2b13

Questions about Virtual Screening

• github.com/GPTomics/bioSkills/tree/main/chemoinformatics/virtual — the audited source at commit c8c1fc3a2b13 and every finding aboveObserved · 19 Oct 2026

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