Agent Skills
› synthetic-sciences/openscience
› molecular-rag
molecular-rag
GitHub基于MolRAG，通过检索ChEMBL/ZINC中结构相似且有实验数据的化合物，为LLM预测提供事实依据，防止幻觉。适用于性质预测、先导优化和SAR分析，不用于批量查询或从头生成。
触发场景
分子性质预测前需要背景数据
先导化合物优化需参考类似骨架
评估新分子的 novelty
基于实验数据进行构效关系推理
安装
npx skills add synthetic-sciences/openscience --skill molecular-rag -g -y
SKILL.md
Frontmatter
{
"name": "molecular-rag",
"tags": [
"drug-discovery",
"RAG",
"retrieval",
"ChEMBL",
"analog-search",
"grounding"
],
"license": "MIT",
"version": "1.0.0",
"category": "chemistry",
"metadata": {
"skill-author": "Synthetic Sciences"
},
"description": "Retrieve structurally similar compounds with known properties from ChEMBL\/ZINC to ground predictions and inform optimization. Based on MolRAG (Xian 2025, ACL).",
"dependencies": [
"rdkit-pypi",
"requests",
"pandas"
]
}
Molecular RAG (Retrieval-Augmented Generation)
Overview
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:
- MolRAG (Xian et al., 2025, ACL): RAG for molecular property prediction — retrieves similar compounds to ground LLM predictions
When to Use This Skill
- Before property prediction: Retrieve analogs with known properties for context
- Lead optimization: Find what modifications worked for similar scaffolds
- Novelty assessment: Check if your generated molecule is truly novel or already known
- SAR grounding: Ground structure-activity reasoning in experimental data
Do NOT use this skill for:
- Bulk database queries (use
chembl-databaseorpubchem-databasedirectly) - De novo generation (use
denovo-design)
Related Skills
- chembl-database: Direct ChEMBL API access
- pubchem-database: PubChem compound lookup
- zinc-database: ZINC compound search
- admet-reasoning: Interpret properties of retrieved analogs
Installation
pip install rdkit-pypi requests pandas
Core Workflows
1. Find Similar Compounds with Known Properties
python scripts/retrieve_analogs.py \
--smiles "c1ccc(NC(=O)c2ccccc2Cl)cc1" \
--similarity-threshold 0.6 \
--max-results 20 \
--output analogs.json
2. Target-Specific Analog Search
python scripts/retrieve_analogs.py \
--smiles "c1ccc(NC(=O)c2ccccc2Cl)cc1" \
--target CHEMBL25 \
--output target_analogs.json
3. SAR Context for Optimization
python scripts/retrieve_analogs.py \
--smiles "c1ccc(NC(=O)c2ccccc2Cl)cc1" \
--include-activities \
--output sar_context.json
Script Reference
| Script | Purpose | Key Outputs |
|---|---|---|
retrieve_analogs.py |
Find similar compounds with experimental data | JSON with analogs, similarities, bioactivities |
版本历史
- e9844a4 当前 2026-07-11 17:21
依赖关系
-
required
rdkit-pypi -
required
requests -
required
pandas
