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rdkit

8.3

by K-Dense-AI

82Favorites
432Upvotes
0Downvotes

Cheminformatics toolkit for fine-grained molecular control. SMILES/SDF parsing, descriptors (MW, LogP, TPSA), fingerprints, substructure search, 2D/3D generation, similarity, reactions. For standard workflows with simpler interface, use datamol (wrapper around RDKit). Use rdkit for advanced control, custom sanitization, specialized algorithms.

chemistry

8.3

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0

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Quick Review

Exceptional cheminformatics skill with comprehensive coverage of RDKit capabilities. The description clearly articulates when to use rdkit vs datamol (advanced control vs standard workflows). SKILL.md is exceptionally well-structured with 12 detailed capability sections covering molecular I/O, sanitization, descriptors, fingerprints, substructure searching, reactions, 3D generation, visualization, and more. Each section includes multiple code examples, parameter explanations, and practical guidance. The skill provides substantial task knowledge with complete workflows for drug-likeness analysis, similarity screening, and substructure filtering. Best practices, error handling, performance optimization, and common pitfalls are thoroughly documented. References to additional files (api_reference.md, descriptors_reference.md, smarts_patterns.md, example scripts) enhance utility without cluttering the main document. High novelty score justified as RDKit's specialized cheminformatics operations (SMARTS pattern matching, reaction SMARTS, pharmacophore features, conformer generation) would require significant tokens and domain expertise for a CLI agent to execute correctly. The skill meaningfully reduces computational cost for molecular analysis tasks. Minor deduction in structure only because the document is quite lengthy, though this is appropriate given the library's complexity and the clear sectioning maintains readability.

LLM Signals

Description coverage10
Task knowledge10
Structure9
Novelty8

GitHub Signals

6,871
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Last commit 1 days ago

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K-Dense-AI

K-Dense-AI

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K-Dense-AI

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