datamol

Solid

Pythonic wrapper around RDKit with simplified interface and sensible defaults. Preferred for standard drug discovery: SMILES parsing, standardization, descriptors, fingerprints, clustering, 3D conformers, parallel processing. Returns native rdkit.Chem.Mol objects. For advanced control or custom parameters, use rdkit directly.

Data & Documents 27,705 stars 2858 forks Updated today MIT

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Skill Content

# Datamol Cheminformatics Skill ## Overview Datamol is a Python library that provides a lightweight, Pythonic abstraction layer over RDKit for molecular cheminformatics. Simplify complex molecular operations with sensible defaults, efficient parallelization, and modern I/O capabilities. All molecular objects are native `rdkit.Chem.Mol` instances, ensuring full compatibility with the RDKit ecosystem. **Key capabilities**: - Molecular format conversion (SMILES, SELFIES, InChI) - Structure standardization and sanitization - Molecular descriptors and fingerprints - 3D conformer generation and analysis - Clustering and diversity selection - Scaffold and fragment analysis - Chemical reaction application - Visualization and alignment - Batch processing with parallelization - Cloud storage support via fsspec ## Installation and Setup Guide users to install datamol: ```bash uv pip install datamol ``` **Import convention**: ```python import datamol as dm ``` ## Core Workflows ### 1. Basic Molecule Handling **Creating molecules from SMILES**: ```python import datamol as dm # Single molecule mol = dm.to_mol("CCO") # Ethanol # From list of SMILES smiles_list = ["CCO", "c1ccccc1", "CC(=O)O"] mols = [dm.to_mol(smi) for smi in smiles_list] # Error handling mol = dm.to_mol("invalid_smiles") # Returns None if mol is None: print("Failed to parse SMILES") ``` **Converting molecules to SMILES**: ```python # Canonical SMILES smiles = dm.to_smiles(mol) # Isomeric SMILES (includ...

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Author
davila7
Repository
davila7/claude-code-templates
Created
11 months ago
Last Updated
today
Language
Python
License
MIT

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datamol

Pythonic wrapper around RDKit with simplified interface and sensible defaults. Preferred for standard drug discovery: SMILES parsing, standardization, descriptors, fingerprints, clustering, 3D conformers, parallel processing. Returns native rdkit.Chem.Mol objects. For advanced control or custom parameters, use rdkit directly.

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datamol

Pythonic wrapper around RDKit with simplified interface and sensible defaults. Preferred for standard drug discovery including SMILES parsing, standardization, descriptors, fingerprints, clustering, 3D conformers, parallel processing. Returns native rdkit.Chem.Mol objects. For advanced control or custom parameters, use rdkit directly.

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datamol

Pythonic wrapper around RDKit with simplified interface and sensible defaults. Preferred for standard drug discovery including SMILES parsing, standardization, descriptors, fingerprints, clustering, 3D conformers, parallel processing. Returns native rdkit.Chem.Mol objects. For advanced control or custom parameters, use rdkit directly.

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rdkit

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.

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rdkit

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.

27,705 Updated today
davila7