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deepchem

Load molecular data, featurize it, and train predictive models.

4|1|Updated Jun 18, 2025
Search Tags:#benchmarks#molecule#graph-neural-networks#deepchem#transfer-learning#property-prediction#molnet
One-click install
npx skills add https://github.com/HolobiomicsLab/Toolomics --skill deepchem-holobiomicslab
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: deepchem
Source: https://github.com/HolobiomicsLab/Toolomics/tree/main/mcp_host/skills/scientific-skills/scientific-skills/deepchem
Command: npx skills add https://github.com/HolobiomicsLab/Toolomics --skill deepchem-holobiomicslab

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires deepchem, numpy, and includes scripts (resource) and references (resource) components.

What problem does it solve?

Molecular ML workflow for chemistry enables researchers to load diverse molecular data, featurize it, and train predictive models.

Core Features & Use Cases

  • Data loading and featurization: CSVLoader, SDFLoader, MolGraphConvFeaturizer, RAW featurizers, MoleculeNet benchmarks
  • Modeling and training: MultitaskRegressor, GCNModel, GROVER, MolGAN, transfer learning
  • Benchmarks and reproducibility: MoleculeNet datasets, pretrained models, evaluation pipelines
  • Workflow templates: Example scripts in scripts/ for solubility, graph models, and transfer learning

Quick Start

Run the provided scripts to train a solubility predictor on Delaney or your own data.

Frequently Asked Questions about deepchem

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I load molecular data and train a graph neural network for property prediction?▼

To train a graph neural network for property prediction, load molecular data using CSVLoader or SDFLoader, apply MolGraphConvFeaturizer, and train models like GCNModel using provided workflow scripts.

Can I use pretrained models like ChemBERTa for transfer learning on custom datasets?▼

Yes, you can use transfer learning with custom datasets by leveraging compatibility with pretrained models from HuggingFace, GROVER, and ChemBERTa within reproducible molecular ML pipelines.

What MoleculeNet benchmarks are available for evaluating molecular machine learning models?▼

MoleculeNet benchmarks are ready-made datasets included for evaluating molecular machine learning models, enabling reproducible pipelines across chemistry, materials science, and protein analysis tasks.

Does this workflow support featurization for both graph and sequence molecular models?▼

Yes, the workflow supports featurization for graph and sequence molecular models by providing RAW featurizers and MolGraphConvFeaturizer to prepare data for architectures like GROVER and MolGAN.

What's the best way to predict molecular solubility using deepchem?▼

The best way to predict molecular solubility is by running provided example scripts to train a MultitaskRegressor on the Delaney dataset or your own custom molecular data.

Do I need numpy to run molecular machine learning pipelines with deepchem?▼

Yes, numpy is a required dependency alongside deepchem to execute molecular machine learning pipelines, load diverse datasets, and train predictive models across chemistry applications.

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