molfeat

To convert SMILES strings into numerical features for QSAR modeling, you can use molecular featurization to transform chemical structures into vectors. This process generates ECFP, MACCS, descriptors, and pretrained embeddings suitable for machine learning inputs.

Generating ChemBERTa embeddings for virtual screening is best handled by using pretrained molecular featurization models. This Skill provides over 100 pretrained embeddings, including ChemBERTa, to convert molecules into numerical representations for deep learning workflows.

Yes, you can use RDKit molecules directly for molecular featurization in deep learning. The featurization process accepts both SMILES strings and RDKit molecules as inputs to generate numerical feature vectors for your models.

Yes, molfeat supports both hand-crafted featurizers and pretrained models for similarity searching. It includes ECFP, MACCS, descriptors, and over 100 pretrained embeddings to generate numerical representations for molecular similarity tasks.

To calculate ECFP features for a SMILES string using the command line, run `molfeat calc ecfp CC(C)O`. This directly generates the molecular features needed for machine learning and virtual screening tasks.