Does this skill support protein and DNA sequence analysis?

Yes, DeepChem includes FASTALoader and sequence-based models like ProtBERT specifically for biological sequence analysis.

Why should I use ScaffoldSplitter instead of RandomSplitter?

ScaffoldSplitter ensures molecules with the same chemical core are grouped together, preventing data leakage and providing a more realistic assessment of how a model generalizes to new scaffolds.

Can I use pretrained models with this skill?

Absolutely. The skill supports transfer learning using pretrained models like ChemBERTa, GROVER, and MolFormer for improved performance on limited data.

What is the best featurizer for small datasets in DeepChem?

For small datasets (less than 1,000 samples), CircularFingerprint (ECFP) or RDKitDescriptors are recommended for their stability and baseline performance.

DeepChem Cheminformatics

Name: DeepChem Cheminformatics
Author: lifangda

bylifangda

•

Ciencia de Datos y ML

Applies machine learning to chemistry, biology, and materials science to predict molecular properties and design new compounds.

Acerca de

This skill integrates DeepChem into the Claude Code environment, providing specialized guidance for molecular machine learning workflows. It enables users to featurize chemical structures using ECFP or graph representations, train sophisticated Graph Neural Networks (GNNs), and leverage MoleculeNet benchmarks for tasks like ADMET prediction and toxicity screening. Whether you're a computational chemist or a data scientist in drug discovery, this skill streamlines the process of loading SMILES strings, splitting data with ScaffoldSplitter to prevent leakage, and fine-tuning pretrained chemical transformers.

Características Principales

Molecular featurization including Circular Fingerprints and Graph representations
Transfer learning support with models like ChemBERTa and GROVER
Access to MoleculeNet benchmark datasets for standardized evaluation
Scaffold-based data splitting to ensure realistic model validation
16 GitHub stars
Implementation of Graph Neural Networks like GCN, GAT, and MPNN

Casos de Uso

Predicting ADMET, toxicity, and solubility properties for drug discovery
Analyzing materials and crystal structures for bandgap and formation energy
Fine-tuning pretrained chemical transformers on specialized small datasets

Acerca de

Características Principales

Molecular featurization including Circular Fingerprints and Graph representations
Transfer learning support with models like ChemBERTa and GROVER
Access to MoleculeNet benchmark datasets for standardized evaluation
Scaffold-based data splitting to ensure realistic model validation
16 GitHub stars
Implementation of Graph Neural Networks like GCN, GAT, and MPNN

Casos de Uso

Predicting ADMET, toxicity, and solubility properties for drug discovery
Analyzing materials and crystal structures for bandgap and formation energy
Fine-tuning pretrained chemical transformers on specialized small datasets