Why is ScaffoldSplitter recommended for molecules?

ScaffoldSplitter ensures that molecules with similar core structures are grouped together, preventing data leakage and providing a more realistic evaluation of a model's ability to generalize to new chemical space.

How does this skill handle molecular data?

The skill provides guidance on loading and featurizing various formats including SMILES strings, SDF files, and protein sequences into ML-ready representations.

Can I use pretrained models with this skill?

Yes, it includes workflows for transfer learning using large-scale pretrained models like ChemBERTa and GROVER, which are especially useful for small datasets.

What is DeepChem used for?

DeepChem is a Python library used to apply machine learning to chemistry, biology, and materials science, particularly for drug discovery and molecular property prediction.

DeepChem Molecular ML

Name: DeepChem Molecular ML
Author: Microck

byMicrock

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データサイエンスとML

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

概要

DeepChem is a powerful open-source library that streamlines the application of machine learning to the life sciences, enabling researchers to predict molecular properties, binding affinities, and toxicities with ease. It provides a comprehensive suite of tools for molecular featurization—converting SMILES or SDF files into graph or vector representations—and includes specialized neural network architectures like Graph Convolutional Networks (GCNs) and MPNNs. Whether performing drug discovery, materials design, or protein analysis, this skill helps Claude implement scientific best practices like scaffold splitting to ensure rigorous and accurate modeling.

主な機能

Seamless integration with 30+ MoleculeNet benchmark datasets
81 GitHub stars
Rigorous data splitting techniques including Scaffold and Butina splitters
Molecular featurization using fingerprints, graph representations, and 3D descriptors
Transfer learning workflows with pretrained models like ChemBERTa and GROVER
Built-in support for Graph Neural Networks (GCN, GAT, MPNN, and AttentiveFP)

ユースケース

Predicting ADMET properties and toxicity for lead compounds in drug discovery
Benchmarking ML models against standardized molecular datasets like Tox21 or BBBP
Training custom graph neural networks on chemical or biological datasets

概要

主な機能

Seamless integration with 30+ MoleculeNet benchmark datasets
81 GitHub stars
Rigorous data splitting techniques including Scaffold and Butina splitters
Molecular featurization using fingerprints, graph representations, and 3D descriptors
Transfer learning workflows with pretrained models like ChemBERTa and GROVER
Built-in support for Graph Neural Networks (GCN, GAT, MPNN, and AttentiveFP)

ユースケース

Predicting ADMET properties and toxicity for lead compounds in drug discovery
Benchmarking ML models against standardized molecular datasets like Tox21 or BBBP
Training custom graph neural networks on chemical or biological datasets