What problem does it solve?
This Skill simplifies complex molecular operations by providing a Pythonic abstraction layer over RDKit, making it easier to perform tasks like SMILES parsing, descriptors, fingerprints, clustering, and 3D conformers.
Core Features & Use Cases
- Molecular Format Conversion: Convert between SMILES, SELFIES, InChI, and other molecular formats.
- Structure Standardization: Standardize and sanitize molecular structures for consistent analysis.
- Descriptors and Fingerprints: Compute molecular descriptors and fingerprints for similarity analysis.
- Clustering and Diversity Selection: Cluster molecules and select diverse subsets for analysis.
- Scaffold Analysis: Extract Murcko scaffolds and analyze molecular frameworks.
- 3D Conformer Generation: Generate and analyze 3D conformers for molecular visualization and analysis.
- Visualization: Visualize molecules and conformers for clear communication and analysis.
- Chemical Reactions: Apply chemical reactions using SMARTS patterns.
- Use Case: Imagine you have a large library of compounds and want to perform drug-likeness filtering and clustering to identify potential leads.
Quick Start
To get started with datamol, install the skill using the following command:
uv pip install datamol
Once installed, you can import the datamol library and use its functions to perform various tasks, such as:
import datamol as dm
# Example: Convert a SMILES string to a molecule object
mol = dm.to_mol("CCO")
