Retrieve structurally similar compounds with known properties from ChEMBL/ZINC to ground predictions and inform optimization. Based on MolRAG (Xian 2025, ACL).
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See how to installReview the instructions the agent will receive before you install.
LLMs hallucinate molecular properties. This skill grounds predictions by retrieving structurally similar compounds with experimentally measured properties from ChEMBL and ZINC. When the agent says "this compound should have good hERG safety," it can now check what happened with similar compounds in real assays.
Based on:
Do NOT use this skill for:
chembl-database or pubchem-database directly)denovo-design)pip install rdkit-pypi requests pandas
python scripts/retrieve_analogs.py \
--smiles "c1ccc(NC(=O)c2ccccc2Cl)cc1" \
--similarity-threshold 0.6 \
--max-results 20 \
--output analogs.json
python scripts/retrieve_analogs.py \
--smiles "c1ccc(NC(=O)c2ccccc2Cl)cc1" \
--target CHEMBL25 \
--output target_analogs.json
python scripts/retrieve_analogs.py \
--smiles "c1ccc(NC(=O)c2ccccc2Cl)cc1" \
--include-activities \
--output sar_context.json
| Script | Purpose | Key Outputs |
|---|---|---|
retrieve_analogs.py | Find similar compounds with experimental data | JSON with analogs, similarities, bioactivities |
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