Does it support transfer learning?

Yes, it provides implementation patterns for fine-tuning pretrained models like ChemBERTa, GROVER, and MolFormer for specific downstream chemical tasks.

What molecular formats does this skill support?

It supports standard chemical formats including SMILES strings, SDF files, FASTA sequences, and PDB structures through DeepChem's specialized loader classes.

Can I use this skill for materials science?

Yes, DeepChem includes tools for predicting crystal and materials properties using specialized models like CGCNN and MEGNet, which this skill helps implement.

Why is ScaffoldSplitter recommended for molecular data?

ScaffoldSplitter ensures that molecules with the same structural core (scaffold) are grouped into the same split, preventing data leakage and providing a more realistic measure of how models generalize to new chemical space.

DeepChem Molecular ML

Name: DeepChem Molecular ML
Author: Activer007

byActiver007

数据科学与机器学习

Streamlines molecular machine learning workflows for drug discovery, property prediction, and materials science using the DeepChem library.

关于

This skill empowers developers and researchers to leverage the DeepChem Python library for scientific machine learning tasks within Claude. It provides expert guidance on loading chemical data (SMILES, SDF, FASTA), performing sophisticated featurization like circular fingerprints and graph representations, and implementing state-of-the-art models including Graph Neural Networks (GNNs) and pretrained transformers like ChemBERTa. Designed for tasks ranging from ADMET property prediction to benchmarking on MoleculeNet, it ensures best practices like scaffold-based data splitting to prevent data leakage in chemical datasets.

主要功能

Molecular featurization (Fingerprints, GNNs, Descriptors)
Advanced data splitting (Scaffold, Butina, Stratified)
MoleculeNet benchmark dataset integration
Transfer learning with pretrained chemical models
0 GitHub stars
Graph Neural Network implementation (GCN, GAT, MPNN)

使用场景

Benchmarking chemical ML models using MoleculeNet datasets
Predicting ADMET and toxicity properties for drug candidates
Training graph neural networks on molecular structures

关于

主要功能

Molecular featurization (Fingerprints, GNNs, Descriptors)
Advanced data splitting (Scaffold, Butina, Stratified)
MoleculeNet benchmark dataset integration
Transfer learning with pretrained chemical models
0 GitHub stars
Graph Neural Network implementation (GCN, GAT, MPNN)

使用场景

Benchmarking chemical ML models using MoleculeNet datasets
Predicting ADMET and toxicity properties for drug candidates
Training graph neural networks on molecular structures