molfeat
Molecular featurization for ML (100+ featurizers). ECFP, MACCS, descriptors, pretrained models (ChemBERTa), convert SMILES to features, for QSAR and molecular ML.
Security Assessment
About molfeat
Molfeat is a robust Python library designed for molecular featurization, addressing the need for effective representation of chemical structures in machine learning tasks. By converting SMILES strings or RDKit molecules into numerical formats, Molfeat supports a wide range of applications including quantitative structure-activity relationship (QSAR) modeling, virtual screening, and molecular similarity searching. Its comprehensive suite of over 100 pre-trained embeddings and custom featurizers simplifies the process of preparing molecular data for machine learning workflows.
FAQ
How do I install Molfeat?
You can install Molfeat using pip with the command 'pip install molfeat'. For all optional dependencies, use 'pip install "molfeat[all]"'.
What types of molecular representations can Molfeat handle?
Molfeat can handle both SMILES strings and RDKit 'Chem.Mol' objects for molecular representation.
Is Molfeat compatible with scikit-learn?
Yes, Molfeat provides scikit-learn compatible transformers to facilitate batch processing and integration into scikit-learn pipelines.
What are the main use cases for Molfeat?
Molfeat is primarily used for molecular machine learning, virtual screening, similarity searching, chemical space analysis, deep learning, and cheminformatics tasks.
Are there any specific dependencies required for certain features?
Yes, optional dependencies are available for specific featurizers, such as 'molfeat[dgl]' for GNN models and 'molfeat[transformer]' for ChemBERTa and other pretrained models.
Install molfeat
Quick Setup:
- Copy the skill folder to
.claude/skills/ - Claude will automatically detect and use the skill
Repository
K-Dense-AI/claude-scientific-skills