rdkit

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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.

AI & Automation 26,817 stars 2774 forks Updated today MIT

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Skill Content

# RDKit Cheminformatics Toolkit ## Overview RDKit is a comprehensive cheminformatics library providing Python APIs for molecular analysis and manipulation. This skill provides guidance for reading/writing molecular structures, calculating descriptors, fingerprinting, substructure searching, chemical reactions, 2D/3D coordinate generation, and molecular visualization. Use this skill for drug discovery, computational chemistry, and cheminformatics research tasks. ## Core Capabilities ### 1. Molecular I/O and Creation **Reading Molecules:** Read molecular structures from various formats: ```python from rdkit import Chem # From SMILES strings mol = Chem.MolFromSmiles('Cc1ccccc1') # Returns Mol object or None # From MOL files mol = Chem.MolFromMolFile('path/to/file.mol') # From MOL blocks (string data) mol = Chem.MolFromMolBlock(mol_block_string) # From InChI mol = Chem.MolFromInchi('InChI=1S/C6H6/c1-2-4-6-5-3-1/h1-6H') ``` **Writing Molecules:** Convert molecules to text representations: ```python # To canonical SMILES smiles = Chem.MolToSmiles(mol) # To MOL block mol_block = Chem.MolToMolBlock(mol) # To InChI inchi = Chem.MolToInchi(mol) ``` **Batch Processing:** For processing multiple molecules, use Supplier/Writer objects: ```python # Read SDF files suppl = Chem.SDMolSupplier('molecules.sdf') for mol in suppl: if mol is not None: # Check for parsing errors # Process molecule pass # Read SMILES files suppl = Chem.SmilesMolSupplier('mol...

Details

Author
K-Dense-AI
Repository
K-Dense-AI/scientific-agent-skills
Created
7 months ago
Last Updated
today
Language
Python
License
MIT

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