virtual-screening

name	virtual-screening
description	Molecular docking and virtual screening for drug discovery. Use for screening compound libraries against protein targets, predicting binding affinities, and identifying lead candidates. Keywords: docking, virtual screening, molecular docking, binding affinity, lead identification
category	Computational Chemistry
tags	["docking","screening","molecular-modeling","drug-design"]
version	1.0.0
author	Drug Discovery Team
dependencies	["rdkit","openbabel","docking-engine"]

Virtual Screening Skill

Molecular docking and virtual screening capabilities for lead identification.

Quick Start

/virtual-screening EGFR --library compounds.sdf --top 10
/dock "target.pdb" --ligands "ligands.smi" --engine vina
/screen-kinases --scaffold quinazoline --threshold -7.0

Capabilities

1. Molecular Docking

Predict binding poses and affinities for compound-target pairs.

Supported docking engines:

• AutoDock Vina - Fast, widely used
• SMINA - Vina derivative with custom scoring
• DiffDock - Deep learning-based
• GNINA - Graph neural network scoring

2. Virtual Screening

Screen large compound libraries against targets.

Library sources:

• ChEMBL (bioactive compounds)
• PubChem (diverse compounds)
• ZINC (commercially available)
• Enamine (make-on-demand)

3. Binding Affinity Prediction

Estimate binding energies using scoring functions.

4. Pose Analysis

Analyze binding modes and key interactions.

Docking Workflow

1. Target Preparation
   ├── Retrieve PDB structure
   ├── Remove water/ligands
   ├── Add hydrogens
   └── Define binding site

2. Ligand Preparation
   ├── Generate 3D conformations
   ├── Minimize energy
   └── Generate protonation states

3. Docking
   ├── Set grid box
   ├── Run docking engine
   └── Generate poses

4. Analysis
   ├── Score poses
   ├── Analyze interactions
   └── Rank compounds

Output Structure

Docking Results

# Virtual Screening Results: EGFR Kinase

## Summary
| Metric | Value |
|--------|-------|
| Compounds screened | 10,000 |
| Successful dockings | 9,847 |
| Top hits (≤ -8 kcal/mol) | 47 |
| Processing time | 2.5 hours |

## Top 10 Compounds

| Rank | Compound ID | Affinity (kcal/mol) | LE | LLE | Interactions |
|------|-------------|---------------------|-----|-----|--------------|
| 1 | CHEMBL210 | -10.2 | 0.42 | 6.8 | H-bond: Met793, hinge |
| 2 | CHEMBL456 | -9.8 | 0.38 | 6.5 | H-bond: Met793, Lys745 |
| 3 | ZINC12345 | -9.5 | 0.35 | 6.2 | π-π: Phe723 |

## Binding Mode Analysis (Top Hit)

### Compound: CHEMBL210
**Affinity**: -10.2 kcal/mol
**LE**: 0.42
**LLE**: 6.8

**Key Interactions**:
- H-bond with Met793 (hinge region)
- H-bond with Thr854
- π-π stacking with Phe723
- Hydrophobic pocket: Le718, Val726

## Pharmacophore Features

1. **Hinge binder**: N-heterocycle H-bond donor/acceptor
2. **Gatekeeper interaction**: Small hydrophobic group
3. **Solvent front**: Polar substituent
4. **Back pocket**: Extended hydrophobic moiety

## Recommendations

1. **Synthesis priority**: Top 5 compounds
2. **SAR exploration**: Around quinazoline core
3. **Experimental validation**: SPR, ITC binding assays

Scoring Metrics

Metric	Formula	Good Range
Binding affinity	Docking score	≤ -7 kcal/mol
Ligand Efficiency (LE)	Score / Heavy atoms	≥ 0.3
LLE (LipE)	Score - LogP	≥ 6
Size	Heavy atom count	20-40

Running Scripts

# Virtual screening with Vina
python scripts/virtual_screening.py \
  --target EGFR \
  --library data/compounds.sdf \
  --top 50 \
  --output results.json

# Docking with custom settings
python scripts/docking.py \
  --pdb 1m17.pdb \
  --center_x 10.5 \
  --center_y 20.3 \
  --center_z 15.8 \
  --size_x 20 \
  --size_y 20 \
  --size_z 20

# Rescoring with GNINA
python scripts/rescore.py \
  --poses docked_poses.sdf \
  --model gnina \
  --output rescored.json

Requirements

# Core dependencies
pip install rdkit meeko

# Docking engines
# AutoDock Vina: http://vina.scripps.edu/
# SMINA: https://github.com/ccsb-scripps/AutoDock-Vina
# GNINA: https://github.com/gnina/gnina

# Optional
pip install prody pymol-open-source

Reference

• See reference/docking-guide.md for detailed docking protocols
• See reference/scoring-functions.md for scoring function details
• See reference/structure-prep.md for protein preparation

Best Practices

1. Prepare structures carefully: Clean PDB, remove duplicates
2. Validate docking protocol: Re-dock co-crystal ligand
3. Consider multiple poses: Top 3-5 poses per compound
4. Use consensus scoring: Combine multiple scoring functions
5. Check binding modes: Visual inspection of top poses
6. Account for flexibility: Consider induced fit if needed

Common Pitfalls

Pitfall	Solution
Incorrect binding site	Validate with known inhibitor
Poor ligand preparation	Generate multiple conformations
Single scoring function	Use consensus scoring
Ignoring protein flexibility	Use ensemble docking
Overinterpreting scores	Remember scoring is approximate

Limitations

• Scoring accuracy: ±2 kcal/mol typical error
• Protein flexibility: Limited in standard docking
• Solvent effects: Often implicit/explicit simplified
• Binding kinetics: Not predicted (affinity only)
• Synthetic accessibility: Not assessed