datamol

Parse, standardize, and analyze molecular structures using datamol.

18|2|Updated Feb 21, 2026
One-click install
npx skills add https://github.com/omar-A-hassan/medsci-agent --skill datamol-omar-a-hassan
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: datamol
Source: https://github.com/omar-A-hassan/medsci-agent/tree/main/.opencode/skills/datamol
Command: npx skills add https://github.com/omar-A-hassan/medsci-agent --skill datamol-omar-a-hassan

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Datamol provides a concise, RDKit-backed API for molecular manipulation, enabling SMILES parsing, standardization, descriptors, and fingerprinting to accelerate cheminformatics workflows.

Core Features & Use Cases

  • SMILES parsing and standardization for clean molecular representations
  • Descriptor calculation and fingerprint generation for similarity search and property prediction
  • Scaffold extraction and visualization to support compound prioritization

Quick Start

Parse a SMILES string, standardize it, and generate a canonical SMILES using datamol.

Frequently Asked Questions about datamol

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

FAQPage Schema
How do I parse and standardize SMILES strings for cheminformatics workflows?

To parse and standardize SMILES strings, you can use datamol to clean molecular representations and generate canonical SMILES. It provides an RDKit-backed API that simplifies molecular manipulation and handles invalid inputs gracefully by returning None.

What is the best way to calculate molecular descriptors and generate fingerprints in Python?

Calculating molecular descriptors and generating fingerprints for similarity search is best handled using datamol. Built on RDKit, it offers a concise API to compute molecular properties needed for property prediction and drug discovery workflows.

Can I extract scaffolds from molecular structures to support compound prioritization?

Yes, you can extract scaffolds from molecular structures to support compound prioritization. Datamol includes features for scaffold extraction and visualization, helping you analyze and prioritize chemical compounds during drug discovery.

Does datamol work with RDKit for molecular manipulation tasks?

Datamol works directly with RDKit, providing an RDKit-backed API for molecular manipulation. It integrates with the datamol API to offer a simplified interface for SMILES normalization, descriptor calculation, and fingerprint generation.

How does datamol handle invalid SMILES inputs during molecular parsing?

Datamol handles invalid SMILES inputs gracefully by returning None instead of raising errors. This ensures your cheminformatics workflows continue running without interruption when encountering malformed or unparsable molecular structures.

When do I need SMILES normalization in cheminformatics workflows?

SMILES normalization is needed when preparing molecular data for descriptor calculation, fingerprint generation, or similarity search. Datamol standardizes molecular representations to ensure consistent inputs for downstream drug discovery and property prediction tasks.