How do I install the Molecular RAG skill?

Run npx skillmds@latest add synthetic-sciences/molecular-rag in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

Is Molecular RAG safe to use?

This skill has not completed SkillMD's automated safety review yet. SkillMD never runs a skill's scripts for you; review the SKILL.md before installing.

Which AI agents work with Molecular RAG?

This skill is tagged as working with Claude Code, Claude.ai, OpenAI Codex. SKILL.md is an open format, so most agents that read a skills directory can load it too.

Is Molecular RAG free to use?

Yes. Installing skills from SkillMD is free. This skill is licensed under MIT.

Who published Molecular RAG?

synthetic-sciences (@synthetic-sciences) published this skill. Their other Agent Skills are listed on their SkillMD profile.

Molecular RAG by synthetic-sciences

Name: Molecular RAG
Author: synthetic-sciences

File contents

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-database or pubchem-database directly)
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

---name: molecular-ragdescription: Retrieve structurally similar compounds with known properties from ChEMBL/ZINC to ground predictions and inform optimization. Based on MolRAG (Xian 2025, ACL).license: MIT---# Molecular RAG (Retrieval-Augmented Generation)## OverviewLLMs 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-database` or `pubchem-database` directly)- 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```bashpip install rdkit-pypi requests pandas```## Core Workflows### 1. Find Similar Compounds with Known Properties```bashpython 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```bashpython scripts/retrieve_analogs.py \ --smiles "c1ccc(NC(=O)c2ccccc2Cl)cc1" \ --target CHEMBL25 \ --output target_analogs.json```### 3. SAR Context for Optimization```bashpython 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 |

Frequently asked questions

Molecular RAG