Molecular ML with diverse featurizers and pre-built datasets. Use for property prediction (ADMET, toxicity) with traditional ML or GNNs when you want extensive featurization options and MoleculeNet benchmarks. Best for quick experiments with pre-trained models, diverse molecular representations. For graph-first PyTorch workflows use torchdrug; for benchmark datasets use pytdc.
How to install deepchem?
synthetic-sciences/openscience/deepchem
$npx -y skills add synthetic-sciences/openscience --skill deepchemInstalls into the current project.
›Prefer a prompt? Paste this to your agent
Run `npx skills use "https://github.com/synthetic-sciences/openscience" --skill "synthetic-sciences/openscience/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/synthetic-sciences/openscience" that are relevant to the current task. Run `npx skills add "https://github.com/synthetic-sciences/openscience"` and select the relevant skills, then follow their instructions.
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