datamol-cheminformatics

Standardize molecular data by parsing and validating SMILES strings to generate canonical representations. This skill handles batch processing for large libraries, ensuring compounds are consistently formatted for downstream analysis.

Compute ECFP fingerprints by using a Pythonic interface that abstracts RDKit for molecular data manipulation. It generates molecular descriptors and fingerprints needed to train machine learning models for drug discovery.

Molecular clustering groups compounds by structural similarity to select diverse subsets. This skill performs similarity searches and scaffold analysis, allowing you to identify the most distinct molecules for further screening.

Yes, you can generate 3D conformers from SMILES. This skill abstracts RDKit to handle 3D molecular structure generation and analysis, providing a Pythonic interface for creating and evaluating conformers.

Yes, it supports cloud storage integration via fsspec. This allows you to directly load and process molecular data from remote storage during batch processing without needing local copies.

Scaffold analysis and fragmentation are needed to extract structural cores and fragment molecules for design insights. This skill simplifies these tasks to help understand structural variations within compound libraries.