Is MoleculeNet supported?

Yes, the skill provides quick access patterns for over 30 MoleculeNet benchmark datasets for standardized model evaluation and comparison.

Does it support Graph Neural Networks?

Absolutely, it includes implementation patterns for GCN, GAT, MPNN, and other graph-based architectures optimized for molecular data.

What is the DeepChem Claude Skill?

It is a specialized capability that guides Claude in implementing molecular machine learning workflows, from data loading and featurization to model training and evaluation.

Can I use this for drug discovery tasks?

Yes, it is specifically designed for ADMET prediction, toxicity modeling, and binding affinity prediction using chemical-specific best practices.

How does it handle chemical data splitting?

The skill emphasizes using ScaffoldSplitter, which ensures that training and test sets contain distinct molecular cores to prevent data leakage and provide realistic performance estimates.

DeepChem Molecular ML

Name: DeepChem Molecular ML
Author: Microck

byMicrock

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Ciencia de Datos y ML

Enables advanced molecular machine learning and drug discovery workflows using specialized featurizers and graph neural networks.

DeepChem is a powerful capability that integrates molecular machine learning into Claude’s development environment, facilitating tasks in chemistry, materials science, and biology. It provides specialized tools for loading chemical data formats like SMILES and SDF, converting molecules into ML-ready representations via fingerprints or graph featurizers, and implementing state-of-the-art models such as Graph Neural Networks (GNNs) and Transformers. This skill is essential for researchers and developers building property prediction models for ADMET or toxicity, performing MoleculeNet benchmarking, or applying transfer learning with pretrained chemical models like ChemBERTa and GROVER.

Características Principales

01Molecular featurization including Circular Fingerprints (ECFP) and Graph representations

02Implementation of Graph Neural Networks (GNNs) including GCN, GAT, and MPNN

03Chemical-specific data splitting methods like ScaffoldSplitter to prevent data leakage

04Support for transfer learning with pretrained models such as ChemBERTa and GROVER

05Seamless access to MoleculeNet benchmark datasets like Tox21, BBBP, and Delaney

0681 GitHub stars

Casos de Uso

01Predicting molecular properties such as solubility, toxicity, and binding affinity for drug discovery

02Benchmarking new machine learning architectures on standardized chemical datasets

03Analyzing protein sequences and crystal structures for materials science applications

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add microck/ordinary-claude-skills deepchem

For use in Claude.ai and ChatGPT

Download Skill