datamol

Parse, standardize, and analyze molecular structures with RDKit integration.

Updated Mar 10, 2026
One-click install
npx skills add https://github.com/felixboehm/biochem-allergy --skill datamol-felixboehm
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: datamol
Source: https://github.com/felixboehm/biochem-allergy/tree/main/.claude/skills/datamol
Command: npx skills add https://github.com/felixboehm/biochem-allergy --skill datamol-felixboehm

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill simplifies complex molecular operations in drug discovery, making it easier to parse, standardize, analyze, and visualize chemical structures.

Core Features & Use Cases

  • Molecule Handling: Easily convert between SMILES, SELFIES, and RDKit Mol objects. Standardize and sanitize molecular structures.
  • Data Processing: Read and write molecular data from various file formats (SDF, CSV, Excel) including remote storage. Compute descriptors and fingerprints in parallel.
  • Advanced Analysis: Perform clustering, diversity selection, scaffold analysis, and 3D conformer generation.
  • Visualization: Generate high-quality molecule images and grids for reporting and analysis.
  • Use Case: Analyze a library of drug candidates by computing their molecular weight, logP, and generating ECFP fingerprints, then cluster them to identify structural similarities.

Quick Start

Use the datamol skill to convert the SMILES string 'CCO' into an RDKit molecule object.

Frequently Asked Questions about datamol

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I convert SMILES strings into RDKit molecule objects for cheminformatics analysis?

To convert SMILES strings into RDKit molecule objects for cheminformatics analysis, use this Pythonic interface to parse and sanitize molecular structures seamlessly. It simplifies the conversion between SMILES, SELFIES, and RDKit Mol objects.

Can I compute molecular descriptors and fingerprints in parallel for a large library?

Yes, you can compute molecular descriptors and fingerprints in parallel for large libraries. The Skill supports batch processing to calculate properties like molecular weight and logP, and generate ECFP fingerprints efficiently.

Does this tool support reading molecular data from remote SDF and CSV files?

Yes, the tool supports reading molecular data from remote SDF and CSV files. It handles remote file I/O and reads various formats including Excel, allowing you to process chemical structures directly from remote storage.

What is the best way to cluster drug candidates by structural similarity?

The best way to cluster drug candidates by structural similarity is to compute their fingerprints and use the built-in clustering and diversity selection functions. This approach identifies structural relationships across molecular libraries effectively.

Do I need RDKit installed to use this molecule processing skill?

Yes, you need RDKit installed because this molecule processing skill integrates with RDKit for its core cheminformatics functionality. It acts as a Pythonic wrapper around RDKit to simplify drug discovery operations.

Can I generate 3D conformers and visualize chemical structures using Python?

Yes, you can generate 3D conformers and visualize chemical structures using Python. The Skill performs 3D conformer analysis and generates high-quality molecule images and grids for reporting.