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rdkit

Compute cheminformatics descriptors, fingerprints, and SMARTS substructure matches from SMILES/SDF/MOL inputs.

Updated May 24, 2026
Search Tags:#cheminformatics#similarity search#fingerprints#smiles#rdkit#molecular descriptors#substructure smarts
One-click install
npx skills add https://github.com/Estrella-231/Mathematical_modeling_tongmeng --skill rdkit-estrella-231
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: rdkit
Source: https://github.com/Estrella-231/Mathematical_modeling_tongmeng/tree/main/.agents/skills/rdkit
Command: npx skills add https://github.com/Estrella-231/Mathematical_modeling_tongmeng --skill rdkit-estrella-231

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

RDKit streamlines cheminformatics workflows by turning molecular string/structure inputs into validated molecule objects, enabling descriptor calculation, fingerprints, substructure searching, similarity comparisons, and 2D/3D coordinate generation without writing low-level chemistry code.

Core Features & Use Cases

  • Parse and validate structures: Read SMILES/SDF/MOL/MolBlock/InChI into RDKit molecule objects with sanitization and failure detection.
  • Compute descriptors and fingerprints: Calculate physicochemical descriptors (e.g., MW, LogP, TPSA, HBD/HBA) and generate multiple fingerprint types for modeling or screening.
  • Search and compare molecules: Run SMARTS substructure searches, compute similarity (e.g., Tanimoto) for lead finding, and cluster/diversify hits.
  • Prepare structures for downstream tasks: Generate 2D depictions or 3D conformers and reaction products for analysis.
  • Use Case: You have a candidate list of molecules in a SMILES or SDF file and want to compute drug-likeness descriptors and find structurally similar compounds above a similarity threshold.

Quick Start

Use the rdkit skill to compute fingerprints and similarity screening for molecules in your SDF or SMILES dataset.

Frequently Asked Questions about rdkit

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

FAQPage Schema
How do I compute molecular descriptors from SMILES strings for drug-likeness analysis?▼

You can parse SMILES strings into validated molecule objects to compute physicochemical descriptors like MW, LogP, TPSA, and HBD/HBA, enabling drug-likeness analysis for candidate molecules without writing low-level chemistry code.

What is the best way to run SMARTS substructure searches on a list of molecules?▼

The best way to run SMARTS substructure searches is by parsing SMILES or SDF inputs into validated molecule objects, then applying SMARTS pattern matching to filter compounds containing specific structural features.

How does Tanimoto similarity screening work for lead finding?▼

Tanimoto similarity screening generates multiple molecular fingerprints from structural inputs and computes similarity metrics against reference compounds to identify and cluster structurally similar hits for lead finding.

Can I parse SDF and MOL blocks with sanitization failure detection?▼

Yes, you can parse SDF, MOL blocks, SMILES, and InChI into RDKit molecule objects with built-in sanitization checks that detect and flag structural parsing failures during validation.

Does this approach support generating 2D and 3D coordinates for downstream analysis?▼

Yes, this approach supports generating 2D depictions and 3D conformers from parsed molecule objects, preparing structural coordinates for downstream visualization and analysis tasks.

Why do I need fingerprint generation for molecular similarity screening?▼

You need fingerprint generation for molecular similarity screening because it converts molecular structures into comparable numerical representations, enabling the calculation of similarity metrics to cluster and diversify hit compounds.

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