molecule-visualization

# Molecule Visualization Generate publication-quality molecular images for drug discovery, medicinal chemistry, and computational chemistry workflows. This skill provides a comprehensive suite of tools for rendering 2D structural drawings, annotated molecule grids, scaffold decomposition views, protein-ligand interaction diagrams, and interactive 3D molecular viewers. ## When to Use - **2D structure drawings**: Generate clean, high-resolution depictions of small molecules for papers, patents, reports, and presentations. - **Molecule grids**: Compare compound series side-by-side with property annotations (QED, LogP, MW, etc.). - **Scaffold highlighting**: Visualize SAR by decomposing molecules into core scaffolds and R-groups. - **Interaction diagrams**: Summarize protein-ligand binding modes from docking or crystal structures. - **3D interactive views**: Create browser-based 3D viewers for proteins, ligands, and complexes. ## Installation All scripts require Python 3.8+ and the following packages: ```bash # Core (required for all scripts) pip install rdkit-pypi pillow matplotlib # For protein-ligand interaction diagrams pip install biopython # For 3D interactive views pip install py3Dmol # Full installation pip install rdkit-pypi pillow matplotlib biopython py3Dmol ``` ## Core Workflows ### 1. Single Molecule Drawing (`scripts/draw_2d.py`) Render a single molecule as a high-quality PNG or SVG image. ```bash # Basic usage python scripts/draw_2d.py --smiles "c1ccccc1" --output benzene.png # With atom highlighting and title python scripts/draw_2d.py \ --smiles "CC(=O)Oc1ccccc1C(=O)O" \ --output aspirin.svg \ --title "Aspirin" \ --highlight-atoms 0,1,2,3 \ --highlight-color "#4A90D9" # Show atom indices for reference python scripts/draw_2d.py \ --smiles "c1ccc(NC(=O)c2ccccc2)cc1" \ --output benzanilide.png \ --show-atom-indices \ --size 600x400 ``` ### 2. Molecule Grid (`scripts/draw_grid.py`) Compare multiple molecules in a grid layout with optional property annotations. ```bash # From CSV file python scripts/draw_grid.py \ --input compounds.csv \ --output grid.png \ --cols 4 \ --properties "qed,mw,logp" # From comma-separated SMILES python scripts/draw_grid.py \ --input "c1ccccc1,c1ccncc1,c1ccoc1" \ --output ring_comparison.png \ --title "Aromatic Ring Comparison" ``` ### 3. Scaffold Highlighting (`scripts/draw_scaffold.py`) Decompose molecules into scaffolds and R-groups for SAR analysis. ```bash # Manual scaffold specification python scripts/draw_scaffold.py \ --smiles "c1ccc(NC(=O)c2ccccc2Cl)cc1" \ --scaffold "c1ccc(NC(=O)c2ccccc2)cc1" \ --output scaffold.png # Automatic Murcko scaffold detection python scripts/draw_scaffold.py \ --smiles "CC(=O)Oc1ccccc1C(=O)O" \ --scaffold auto \ --output murcko.png # R-group decomposition across analogs python scripts/draw_scaffold.py \ --smiles "c1ccc(NC(=O)c2ccccc2)cc1" \ --scaffold "c1ccc(NC(=O)c2ccccc2)cc1" \ --analogs analogs.csv \ --output rgroup_table.png ``` ### 4. Protein-Ligand Interaction Diagram (`scripts/draw_interactions.py`) Generate 2D interaction diagrams from protein-ligand complexes. ```bash python scripts/draw_interactions.py \ --protein receptor.pdb \ --ligand ligand.sdf \ --output interactions.png \ --distance-cutoff 4.0 ``` ### 5. Interactive 3D View (`scripts/render_3d.py`) Create self-contained HTML files with interactive 3D molecular viewers. ```bash # Protein with cartoon representation python scripts/render_3d.py \ --input protein.pdb \ --output view.html \ --style cartoon \ --color chain # Protein-ligand complex python scripts/render_3d.py \ --input protein.pdb \ --ligand ligand.sdf \ --output complex.html \ --style cartoon ``` ### 6. Pocket Visualization (`scripts/render_3d.py --mode pockets`) Visualize detected binding pockets as colored spheres on the protein surface. Sphere color indicates druggability: green (>0.7), orange (0.4-0.7), red (<0.4). Sphere size is proportional to pocket volume. ```bash # After pocket-detection/detect.py or druggability.py python scripts/render_3d.py \ --input protein.pdb \ --pockets druggability.json \ --mode pockets \ --output pocket_view.html # With specific residues highlighted python scripts/render_3d.py \ --input protein.pdb \ --pockets pockets.json \ --mode pockets \ --highlight-residues "189,195,57" \ --output pocket_view.html ``` ### 7. Docking Results Visualization (`scripts/render_3d.py --mode docking-results`) Overlay top docked poses on the protein, colored by rank (green = best, red = worst). The top-ranked pose gets a translucent surface highlight. ```bash # After molecular-docking/dock.py python scripts/render_3d.py \ --input protein.pdb \ --poses dock_results/poses.sdf \ --mode docking-results \ --top-n 5 \ --output docking_results.html ``` ## Script Reference | Script | Purpose | Key Inputs | |--------|---------|------------| | `draw_2d.py` | Single molecule 2D drawing | SMILES, output path | | `draw_grid.py` | Multi-molecule grid | CSV or SMILES list, output path | | `draw_scaffold.py` | Scaffold and R-group analysis | SMILES, scaffold, output path | | `draw_interactions.py` | Protein-ligand interactions | PDB, SDF, output path | | `render_3d.py` | Interactive 3D viewer | PDB/SDF/SMILES, output HTML | ## Style Guide See `references/style_guide.md` for detailed guidance on: - **Colors**: CPK atom coloring scheme (C=gray, N=blue, O=red, S=yellow, Cl=green, etc.) - **Resolution**: 300 DPI minimum for print; 150 DPI for screen. Vector (SVG) preferred for publications. - **Font sizes**: 12pt minimum for labels in figures; 8pt minimum for atom indices. - **Image dimensions**: Single molecule 400x300px default; grid cells 300x250px; interaction diagrams 800x800px. - **Interaction colors**: Green for H-bonds, gray for hydrophobic, orange for pi-stacking, red for salt bridges. - **Colorblind-friendly**: Prefer blue/orange instead of red/green when accessibility is a concern. - **2D vs 3D**: Use 2D for SAR tables, patent figures, and print publications. Use 3D for binding mode analysis, presentations, and supplementary material.