Molecular Analysis

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Performs cheminformatics and molecular property analysis using RDKit, PubChem, and ChEMBL. Supports SMILES/InChI parsing, molecular descriptor calculation, drug-likeness filtering (Lipinski, Veber), ADMET prediction, substructure search, and structure-activity relationship (SAR) analysis for drug discovery workflows.

cheminformaticsrdkitdrug-discoverymoleculessmilesadmetpubchemchemblsar

Casos de uso

  • Calculating molecular descriptors and drug-likeness properties
  • Virtual screening of compound libraries against target profiles
  • Structure-activity relationship analysis for lead optimization
  • ADMET property prediction for drug candidates
  • Querying PubChem and ChEMBL for bioactivity data
  • Generating molecular fingerprints for similarity search

Prompt de ejemplo

Analyze the following set of SMILES strings for drug-likeness and ADMET properties.

Compounds:
- CC(=O)Oc1ccccc1C(=O)O  (Aspirin)
- CC12CCC3C(C1CCC2O)CCC4=CC(=O)CCC34C  (Testosterone)
- CN1C=NC2=C1C(=O)N(C(=O)N2C)C  (Caffeine)

For each compound:
1. Calculate Lipinski Rule of 5 properties (MW, LogP, HBD, HBA)
2. Assess Veber rules (TPSA, rotatable bonds)
3. Predict basic ADMET flags (solubility, BBB permeability)
4. Generate RDKit Python code for the full analysis pipeline
5. Rank compounds by overall drug-likeness score

Output a summary table and the complete Python script using RDKit.

Modelos recomendados

claude-opus-4-6gpt-5gemini-2-5-pro

Herramientas compatibles

claude-codecursorkiroany

Modalidades

Entrada: text, code, file
→
Salida: text, code

Skills relacionadas

data-analysis

Autor

OpenModels Community

@openmodelsrun