datamol

Parse, standardize, and analyze molecular data with RDKit via a Pythonic interface.

8|Updated Jan 13, 2026

Search Tags:#bioinformatics #drug discovery #cheminformatics #chemistry #rdkit #molecular data

Ready to run

npx skills add https://github.com/hxk622/TokenDance --skill datamol-hxk622

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: datamol
Source: https://github.com/hxk622/TokenDance/tree/main/backend/app/skills/builtin/scientific/chemistry/datamol
Command: npx skills add https://github.com/hxk622/TokenDance --skill datamol-hxk622

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill simplifies complex molecular cheminformatics tasks, enabling faster and more efficient drug discovery and development workflows.

Core Features & Use Cases

Molecular Handling: Parse, standardize, and convert molecular formats (SMILES, SDF, etc.).
Descriptor & Fingerprint Calculation: Compute a wide range of molecular descriptors and fingerprints for analysis and machine learning.
Clustering & Similarity: Group similar molecules and identify diverse sets for screening libraries.
3D Conformer Generation: Generate and analyze 3D structures for conformational analysis.
Use Case: Analyze a library of potential drug candidates by calculating their molecular weight, logP, and generating ECFP fingerprints, then cluster them to identify structurally distinct groups for further investigation.

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 calculate molecular descriptors and ECFP fingerprints for a library of drug candidates?▼

Calculate molecular descriptors and ECFP fingerprints for drug candidates by parsing SMILES or SDF files into RDKit molecule objects. This Skill provides a Pythonic interface for batch processing to compute structural descriptors and generate fingerprints for machine learning analysis.

Can I standardize and convert molecular formats like SMILES and SDF in batch for cheminformatics?▼

Standardize and convert molecular formats like SMILES and SDF in batch for cheminformatics using this Pythonic RDKit interface. It handles molecular parsing and standardization, supporting parallelization to efficiently process large datasets of molecules.

What is the best way to cluster similar molecules and identify diverse screening libraries?▼

Cluster similar molecules and identify diverse screening libraries by computing molecular fingerprints and performing similarity computations. This Skill handles clustering and scaffold analysis to group structurally similar molecules and highlight distinct sets for drug discovery.

Does this RDKit interface support 3D conformer generation for conformational analysis?▼

Yes, this RDKit interface supports 3D conformer generation for conformational analysis. It generates and analyzes 3D molecular structures directly within your Python workflow to facilitate drug discovery and development tasks.

How do I perform scaffold analysis to identify structurally distinct groups in a molecule dataset?▼

Perform scaffold analysis to identify structurally distinct groups in a molecule dataset by applying RDKit's core functionalities through this Skill. It enables scaffold extraction and clustering to evaluate structural diversity across chemical libraries.

Can I visualize molecular data and structures generated from RDKit?▼

Yes, visualize molecular data and structures generated from RDKit using this Skill. It provides visualization capabilities alongside standard cheminformatics tasks, allowing you to render molecular formats and analyze structural outputs directly in Python.