alterlab-deepchem
byalterlab-ieu· 90 skills
Runs molecular machine learning with DeepChem — diverse featurizers, pre-built MoleculeNet benchmark datasets, and pre-trained models (ChemBERTa, GROVER) for property prediction (ADMET, toxicity, solubility) via traditional ML or graph neural networks. Use when running end-to-end molecular ML experiments that need MoleculeNet benchmarks, scaffold splitting, or ready-made models with minimal setup; for building custom PyTorch graph architectures prefer alterlab-torchdrug, and for standalone molecule-to-feature-vector generation prefer alterlab-molfeat. Part of the AlterLab Academic Skills suite.
How to install alterlab-deepchem?
alterlab-ieu/alterlab-academic-skills/alterlab-deepchem
$npx -y skills add alterlab-ieu/alterlab-academic-skills --skill alterlab-deepchemInstalls into the current project.
›Prefer a prompt? Paste this to your agent
Run `npx skills use "https://github.com/alterlab-ieu/alterlab-academic-skills" --skill "alterlab-ieu/alterlab-academic-skills/alterlab-deepchem"` and follow the generated skill instructions now. Read its complete output, redirecting it to a temporary file first if necessary. Resolve relative paths from the supporting-files directory it provides.
Use the skills in "https://github.com/alterlab-ieu/alterlab-academic-skills" that are relevant to the current task. Run `npx skills add "https://github.com/alterlab-ieu/alterlab-academic-skills"` and select the relevant skills, then follow their instructions.
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