Molecular Analysis

İleridataEn az 64K bağlam
Kaynağı görüntüle

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

Kullanım alanları

  • 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

Örnek prompt

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.

Önerilen modeller

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

Uyumlu araçlar

claude-codecursorkiroany

Modaliteler

Giriş: text, code, file
→
Çıkış: text, code

İlgili Skills

data-analysis

Yazar

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