Run end-to-end molecular property prediction workflows with DeepChem.
npx skills add https://github.com/Hung-3008/agusta --skill deepchem-hung-3008
Please help me install this Agent Skill. Skill: deepchem Source: https://github.com/Hung-3008/agusta/tree/main/.agents/skills/deepchem Command: npx skills add https://github.com/Hung-3008/agusta --skill deepchem-hung-3008
DeepChem enables researchers to apply machine learning to chemistry, materials science, and biology by offering modular tools for data loading, molecular featurization, model training, and access to MoleculeNet benchmarks.
Run a quick molecular property prediction workflow on a MoleculeNet benchmark dataset.
High-intent search queries and answers about installing and using this skill.
Molecular property prediction on MoleculeNet benchmarks is executed by loading datasets, selecting a featurizer, splitting data with ScaffoldSplitter, and training a Multitask or GCN model for evaluation.
Molecular machine learning workflows support fingerprint, descriptor, and graph-based featurizers, allowing you to transform raw molecular structures into structured numerical representations for model training.
Transfer learning with pretrained models is fully supported for drug discovery, enabling scalable training on large molecular datasets to improve prediction accuracy while reducing required training time.
ScaffoldSplitter works for splitting custom molecular datasets by separating compounds based on their underlying molecular scaffolds, ensuring structurally distinct train and test sets for robust model evaluation.
Training a Multitask model on custom molecular data involves loading your dataset, applying an appropriate featurizer, and following the provided end-to-end workflow templates that offer specific guidance for custom datasets.
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