Does this skill help with benchmarking?

Yes, it provides built-in loaders for MoleculeNet, a collection of 30+ curated datasets like Tox21 and BBBP, allowing for standardized performance comparisons across different architectures.

Can Claude help me implement transfer learning with molecules?

Absolutely. This skill enables Claude to guide you through fine-tuning pretrained models like ChemBERTa or GROVER on small, domain-specific datasets to improve accuracy.

What is the benefit of using ScaffoldSplitter in DeepChem?

ScaffoldSplitter ensures that molecules with the same structural core are kept together in the same split, preventing data leakage and providing a more realistic performance estimate for novel drug candidates.

Can I use DeepChem for protein sequence analysis?

Yes, DeepChem supports biological sequences through the FASTALoader and provides access to pretrained protein models like ProtBERT for sequence-based property prediction.

Which featurizer should I choose for my model?

DeepChem offers a variety of featurizers: CircularFingerprints are excellent for traditional ML models like Random Forest, while MolGraphConvFeaturizer is designed specifically for Graph Neural Networks.

DeepChem Molecular Machine Learning

Name: DeepChem Molecular Machine Learning
Author: Activer007

byActiver007

データサイエンスとML

Applies machine learning to chemistry, biology, and materials science for drug discovery and molecular property prediction.

概要

DeepChem is a comprehensive Python library integrated as a Claude skill to facilitate advanced machine learning in chemistry, materials science, and biology. It enables Claude to handle complex molecular data formats like SMILES and SDF, perform sophisticated featurization including circular fingerprints and graph representations, and implement state-of-the-art models such as Graph Neural Networks (GNNs) and molecular transformers. This skill is particularly useful for researchers and developers working on drug discovery, toxicity prediction (ADMET), and materials design who require specialized data splitting techniques like ScaffoldSplitter and access to curated benchmarks via MoleculeNet.

主な機能

Direct access to MoleculeNet benchmarks for standardized model evaluation
Support for transfer learning with pretrained models like ChemBERTa and GROVER
0 GitHub stars
Implementation of specialized GCN, GAT, MPNN, and AttentiveFP graph models
Automated loading and processing of SMILES, SDF, and FASTA data formats
Advanced molecular featurization including Fingerprints, GNNs, and RDKit descriptors

ユースケース

Classifying molecular binding affinities using Graph Neural Networks
Predicting ADMET properties and toxicity for drug discovery pipelines
Analyzing protein or DNA sequences for biological property prediction

概要

主な機能

Direct access to MoleculeNet benchmarks for standardized model evaluation
Support for transfer learning with pretrained models like ChemBERTa and GROVER
0 GitHub stars
Implementation of specialized GCN, GAT, MPNN, and AttentiveFP graph models
Automated loading and processing of SMILES, SDF, and FASTA data formats
Advanced molecular featurization including Fingerprints, GNNs, and RDKit descriptors

ユースケース

Classifying molecular binding affinities using Graph Neural Networks
Predicting ADMET properties and toxicity for drug discovery pipelines
Analyzing protein or DNA sequences for biological property prediction