virtual-screening
Molecular docking and virtual screening for drug discovery. Use for screeningcompound libraries against protein targets, predicting binding affinities, andidentifying lead candidates.Keywords: docking, virtual screening, molecular docking, binding affinity, lead identification
When & Why to Use This Skill
This Claude skill facilitates high-throughput virtual screening and molecular docking for drug discovery. It enables researchers to predict binding affinities, analyze molecular interactions, and identify lead candidates by integrating powerful computational chemistry tools and automated workflows. By streamlining the transition from compound libraries to ranked hits, it significantly accelerates the early stages of pharmaceutical research.
Use Cases
- Lead Discovery: Efficiently screening massive compound libraries (e.g., ChEMBL, ZINC, or Enamine) against specific protein targets to identify potential drug candidates.
- Binding Pose Prediction: Utilizing multiple docking engines like AutoDock Vina, SMINA, and DiffDock to predict and visualize how ligands interact with protein binding sites at an atomic level.
- Affinity Ranking & Optimization: Calculating binding energies and ligand efficiency (LE) to prioritize compounds for chemical synthesis and experimental validation based on predicted potency.
- Automated Structure Preparation: Streamlining the preparation of protein PDB files and ligand 3D conformations, ensuring consistent and reproducible docking experiments.
- Pharmacophore Analysis: Identifying key molecular interactions such as H-bonds and π-π stacking to guide SAR (Structure-Activity Relationship) exploration.
| name | virtual-screening |
|---|---|
| description | | |
| 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 |
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
- Prepare structures carefully: Clean PDB, remove duplicates
- Validate docking protocol: Re-dock co-crystal ligand
- Consider multiple poses: Top 3-5 poses per compound
- Use consensus scoring: Combine multiple scoring functions
- Check binding modes: Visual inspection of top poses
- 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