---

name: data-and-molnet description: "Load user data into DeepChem datasets, split datasets, apply transformers, and use MoleculeNet loaders safely." disable-model-invocation: true metadata: disco-role: operating license: MIT

DeepChem Data And MoleculeNet

Use this sub-skill when the task is about deepchem.data, deepchem.splits, deepchem.trans, or deepchem.molnet: loading CSV/SDF/FASTA/image/numpy data, debugging loader columns, creating train/valid/test splits, applying transformers, or choosing MoleculeNet loader options.

Route elsewhere when the user asks for:

• Featurizer internals or molecular representation choices: ../featurization/.
• Model fitting, metrics, prediction, or evaluation loops: ../model-training/.
• Docking, protein complexes, PDBBind structure preparation, or structural biology workflows: ../docking-and-structure/.

Fast Path

1. Identify the source format: CSV table, SDF properties, FASTA sequences, image files, in-memory arrays, or MoleculeNet.
2. Confirm the task column names, feature column (smiles by default for molecular CSV), id column, and whether missing labels should become zero weights.
3. Load into a dc.data.Dataset with CSVLoader, SDFLoader, NumpyDataset, or DiskDataset.from_numpy.
4. Split with dc.splits.RandomSplitter() for generic ML baselines or dc.splits.ScaffoldSplitter() for molecular generalization tests.
5. Fit transformers on the training dataset only, then apply the same transformers to valid/test.
6. For MoleculeNet, decide whether network downloads and disk cache writes are acceptable before calling a loader.

Common APIs

import deepchem as dc

loader = dc.data.CSVLoader(
    tasks=["activity"],
    feature_field="smiles",
    id_field="compound_id",
    featurizer=dc.feat.CircularFingerprint(size=1024),
)
dataset = loader.create_dataset("data.csv")

splitter = dc.splits.ScaffoldSplitter()
train, valid, test = splitter.train_valid_test_split(dataset, seed=123)

transformer = dc.trans.NormalizationTransformer(transform_y=True, dataset=train)
train = transformer.transform(train)
valid = transformer.transform(valid)
test = transformer.transform(test)

For detailed parameter notes, see references/api-reference.md. For complete recipes, see references/workflows.md. For column and shape assumptions, see references/data-formats.md. For failure symptoms and fixes, see references/troubleshooting.md.

Bundled Helpers

• scripts/load_tiny_csv_dataset.py creates or loads a tiny CSV and converts it to a DeepChem dataset.
• scripts/split_tiny_dataset.py builds deterministic tiny splits with RandomSplitter or ScaffoldSplitter.

Run either helper with --help first. They create tiny local demo data by default and do not depend on repository examples.