datamol

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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 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. **Version note:** Examples target **datamol 0.12.x** (PyPI stable: **0.12.5**, June 2024). Since 0.10.0, modules are lazy-loaded by default (set `DATAMOL_DISABLE_LAZY_LOADING=1` to disable). Since 0.12.2, RDKit is a direct PyPI dependency of datamol. Fingerprints use RDKit's `rdFingerprintGenerator` API (0.12.5+). **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 ``` RDKit is installed automatically with datamol. For remote file paths (S3, GCS, HTTP), install the matching fsspec backend: ```bash uv pip install s3fs # AWS S3 uv pip install gcsfs # Google Cloud Storage ``` **Import convention**: ```python import datamol as dm ``` ...

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

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Data & Documents Listed

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.

13 Updated 1 months ago
tassiovale
AI & Automation Featured

alterlab-datamol

Wraps RDKit in a high-level, pandas-friendly datamol interface with sensible defaults for everyday drug discovery — SMILES/SDF loading into DataFrames, molecule standardization, descriptors, fingerprints, Butina clustering, 3D conformer generation, scaffold analysis, and parallel batch processing, returning native rdkit.Chem.Mol objects. Use when running standard cheminformatics pipelines on molecule tables with minimal boilerplate; for low-level control, custom sanitization, or specialized algorithms prefer alterlab-rdkit. Part of the AlterLab Academic Skills suite.

66 Updated 1 weeks ago
AlterLab-IEU
AI & Automation Featured

alterlab-rdkit

Provides the RDKit cheminformatics toolkit for low-level, fine-grained molecular primitives — SMILES/SDF parsing, descriptors (MW, LogP, TPSA), fingerprints, substructure/SMARTS search, 2D/3D coordinate generation, similarity, and reaction handling. Use when custom sanitization, specialized fingerprint or descriptor algorithms, reaction enumeration, or conformer generation demand direct API control; for a high-level pandas-friendly wrapper over RDKit prefer alterlab-datamol, and for turning molecules into ML feature vectors prefer alterlab-molfeat. Part of the AlterLab Academic Skills suite.

66 Updated 1 weeks ago
AlterLab-IEU