molecule-visualization

name molecule-visualization description Publication-quality molecular visualization. 2D structure drawings (PNG/SVG), molecule grids with property annotations, scaffold highlighting, protein-ligand interaction diagrams, and interactive 3D views. category chemistry license MIT metadata {"skill-author":"Synthetic Sciences"} 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: # 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. # 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. # 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. # 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. 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. # 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. # 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. # 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.