Pytdc

PyTDC (Therapeutics Data Commons)

Use the official PyTDC distribution (import tdc) to discover therapeutic ML tasks, load approved datasets, apply task-appropriate splits, evaluate predictions, and work with curated benchmark groups. Prefer package metadata over copied dataset lists, and plan network/storage effects before constructing any loader.

Verified snapshot

• Research date: 2026-07-23
• PyPI stable: PyTDC 1.1.15, released 2025-03-31
• Package/source repository: mims-harvard/TDC
• Code license: MIT
• PyPI supplies only a source distribution and declares no Requires-Python
• The dependency graph makes CPython 3.11 the reproducible target used here: cellxgene-census==1.15.0 excludes Python 3.12, and PyTDC's constrained RDKit release has no CPython 3.13 wheel
• PyTDC imports deprecated pkg_resources at runtime. Setuptools 82 removed that module; pin the verified compatibility release setuptools 80.9.0.
• tdc.readthedocs.io still identifies itself as TDC 0.4.1; use it as API cross-reference, not as release-version evidence
• Upstream publishes no GitHub tags/releases or maintained changelog. Treat undocumented migration claims as uncertainty and verify against the installed 1.1.15 source/metadata.

See references/sources.md for dated evidence and known documentation conflicts.

Installation

Use an isolated CPython 3.11 environment and pin the reviewed snapshot:

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uv venv --python 3.11 .venv-pytdc
uv pip install --dry-run --python .venv-pytdc/bin/python \
  "setuptools==80.9.0" "PyTDC==1.1.15"
uv pip install --python .venv-pytdc/bin/python \
  "setuptools==80.9.0" "PyTDC==1.1.15"

The tested macOS ARM64 resolution installed 123 packages, including large scientific/ML dependencies, so the environment itself can transfer and occupy hundreds of megabytes before any dataset is downloaded. Review the dry run and available disk first. The direct pins identify the reviewed API snapshot; generate a platform-specific uv.lock in the user's project when every transitive version must also be frozen.

For an ephemeral command:

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uv run --python 3.11 \
  --with "setuptools==80.9.0" --with "PyTDC==1.1.15" \
  python scripts/discover_metadata.py --kind tasks

To check for a newer release, inspect the PyPI release history at https://pypi.org/project/pytdc/. Before changing the pin, compare its source distribution, dependencies, official repository, task registries, and smoke tests; do not silently substitute the separate pytdc-nextml package.

Non-negotiable data and network policy

1. Discover first. Reading tdc.metadata or using scripts/discover_metadata.py does not instantiate a loader or download data.
2. Plan second. Record the exact task/dataset, official task page, license, expected size, cache directory, split, metric, and reproducibility seed.
3. Ask the user before downloading. Loader constructors fetch missing data. Some datasets and benchmark-group archives are large; model-backed oracles can fetch checkpoints; remote/docking oracles can transmit molecular structures.
4. Execute only after approval. In bundled CLIs, --execute acknowledges execution and --download is additionally required for MolGen corpora or supported oracle checkpoints.
5. Keep outputs bounded. Emit counts, schema, and small previews rather than full datasets, sequences, prediction arrays, or molecule corpora.

Cache and cost behavior

• Ordinary loaders default to path="./data" and save files beneath that path. The bundled scripts instead default to explicit .pytdc-* directories.
• Core downloads use Harvard Dataverse file endpoints when a local filename is absent. Newer resource classes may use other upstream services.
• admet_group(path=...) and other benchmark-group constructors download and extract the group archive when <path>/<group> is absent.
• Download-backed Oracle(...) construction uses ./oracle internally. The bundled oracle CLI changes into a safe runtime directory before approved calls.
• PyTDC 1.1.15 does not provide a universal cache quota, eviction policy, or dataset-wide checksum manifest. Use scripts/cache_audit.py and manage disk retention explicitly.
• Network transfer, local storage, decompression, parsing, feature generation, docking, and external service calls can all incur time or monetary cost.

The PyTDC code is MIT. Dataset/task licenses are heterogeneous: official task pages include per-dataset terms ranging from Creative Commons licenses to non-commercial restrictions or “Not Specified.” Verify the exact dataset's page and original source terms before download, redistribution, publication, or commercial use. Cite both TDC and the original dataset.

Start with metadata-only discovery

From this skill directory:

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uv run --python 3.11 --with "setuptools==80.9.0" --with "PyTDC==1.1.15" \
  python scripts/discover_metadata.py --kind datasets --task ADME --limit 50

uv run --python 3.11 --with "setuptools==80.9.0" --with "PyTDC==1.1.15" \
  python scripts/discover_metadata.py --kind benchmarks --limit 50

uv run --python 3.11 --with "setuptools==80.9.0" --with "PyTDC==1.1.15" \
  python scripts/discover_metadata.py --kind evaluators --limit 100

The package API is also metadata-only:

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from tdc.utils import retrieve_dataset_names, retrieve_benchmark_names

adme_names = retrieve_dataset_names("ADME")
admet_benchmarks = retrieve_benchmark_names("admet_group")

Use exact returned names. PyTDC performs fuzzy matching internally, but explicit matching avoids silently selecting the wrong dataset/oracle.

Dataset workflow

Plan a split without downloading:

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uv run --python 3.11 --with "setuptools==80.9.0" --with "PyTDC==1.1.15" \
  python scripts/load_and_split_data.py \
  --task ADME --dataset Caco2_Wang --method scaffold \
  --seed 42 --data-dir .pytdc-data

After the user approves the dataset, license, transfer, and storage:

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uv run --python 3.11 --with "setuptools==80.9.0" --with "PyTDC==1.1.15" \
  python scripts/load_and_split_data.py \
  --task ADME --dataset Caco2_Wang --method scaffold \
  --seed 42 --data-dir .pytdc-data --execute

Verified public import patterns include:

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from tdc.single_pred import ADME, Tox
from tdc.multi_pred import DDI, DTI
from tdc.generation import MolGen, Reaction, RetroSyn

Constructors perform data access, so do not run them before approval:

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data = ADME(name="Caco2_Wang", path=".pytdc-data")
frame = data.get_data(format="df")
split = data.get_split(
    method="scaffold",
    seed=42,
    frac=[0.7, 0.1, 0.2],
)
# split keys are: train, valid, test

Read references/datasets.md before choosing a task or dataset.

Split selection without overclaiming leakage control

• random: default for loaders; default seed 42 and fractions 0.7/0.1/0.2.
• scaffold: documented generic support for molecule-based ADME, Tox, and HTS. PyTDC groups RDKit Bemis–Murcko scaffold strings (chirality disabled), but that does not prove absence of analog, duplicate, label, temporal, or provenance leakage.
• cold_split: multi-instance API. Pass exact dataframe columns, for example method="cold_split", column_name=["Drug", "Target"]. Multi-column splitting can discard cross-partition rows and need not preserve requested row fractions.
• combination: built-in DrugSyn combination split.
• time: pair-loader API requiring time_column; the verified built-in case is BindingDB_Patent with its Year column. The API spelling is time, not temporal.

Do not use undocumented cold_drug_target, temporal, or stratified=True examples. For every split, record PyTDC version, parameters, row counts, and exact entity overlap audits. PyTDC 1.1.15's random splitter uses the supplied seed for test sampling but a fixed random_state=1 for validation sampling; do not describe all partitions as independently varying with the seed.

Detailed semantics and caveats are in references/utilities.md.

Evaluators

Use exact names from the installed evaluator registry:

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from tdc import Evaluator

mae = Evaluator(name="MAE")(y_true, y_pred)
auroc = Evaluator(name="ROC-AUC")(y_true_binary, predicted_scores)
pcc = Evaluator(name="PCC")(y_true, y_pred)

PCC is the registered Pearson-correlation name; Pearson is not. Multi-class registry names are micro-f1, macro-f1, and kappa. Thresholded binary metrics default to 0.5. Metric direction and input shape are metric-specific; use the official task/benchmark metric rather than choosing from task type alone.

Benchmark groups

Use specialized classes. Top-level from tdc import BenchmarkGroup is retained only as a deprecated compatibility path in 1.1.15.

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from tdc.benchmark_group import admet_group

# Run only after approval: construction may download the group archive.
group = admet_group(path=".pytdc-benchmarks")
benchmark = group.get("Caco2_Wang")
train_val = benchmark["train_val"]
test = benchmark["test"]
train, valid = group.get_train_valid_split(
    seed=1,
    benchmark=benchmark["name"],
    split_type="default",
)

For one run, group.evaluate({name: test_predictions}) returns metric results. For leaderboard aggregation, pass a list of at least five prediction dictionaries to group.evaluate_many(...). Do not index group.get(...) by seed, and do not derive dummy predictions from test labels.

Use scripts/benchmark_evaluation.py to validate a bounded JSON prediction plan before any group download. See references/utilities.md for the exact JSON shape and API behavior.

Molecular generation and oracles

PyTDC supplies molecule corpora, evaluators, and oracles; it does not train or provide a generic molecule generator in the core workflow. Discover current names:

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uv run --python 3.11 --with "setuptools==80.9.0" --with "PyTDC==1.1.15" \
  python scripts/discover_metadata.py --kind oracles --limit 100

Plan bounded local QED scoring:

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uv run --python 3.11 --with "setuptools==80.9.0" --with "PyTDC==1.1.15" \
  python scripts/molecular_generation.py score --oracle QED --smiles CCO

Add --execute only after review. LogP and SA call the downloadable fpscores artifact in 1.1.15; they and DRD2/GSK3B/JNK3/CYP3A4_Veith also require --download. The helper intentionally refuses remote services, docking, distribution, and composite oracles. It preserves input order and never assumes score direction.

Read references/oracles.md before any oracle call.

Bundled resources

Scripts

• scripts/discover_metadata.py — download-free package registry discovery
• scripts/load_and_split_data.py — task-aware split plan/explicit execution
• scripts/benchmark_evaluation.py — prediction validation and explicit evaluation
• scripts/molecular_generation.py — bounded local/checkpoint scoring and MolGen plan
• scripts/cache_audit.py — read-only bounded cache manifest

Every CLI uses lazy optional imports, safe relative output/cache paths, JSON summaries, bounded output, and no implicit dataset/model download.

References

• references/datasets.md — task discovery, data access, cache behavior, and licensing
• references/utilities.md — splits, evaluators, and benchmark-group APIs
• references/oracles.md — oracle categories, side effects, and safe execution
• references/sources.md — dated authoritative sources and unresolved upstream gaps

Composing with the rest of the bundle

• chembl → before: PyTDC ships curated benchmark sets; ChEMBL is where a dataset for your target comes from. The split discipline here applies to both.
• rdkit / datamol / molfeat → alongside: standardisation and featurisation. PyTDC supplies the data and the split; it does not choose your representation.
• deepchem → after: the training loop, once the split is decided.
• admet-prediction → instead: ADMET-AI is trained on 41 of these same TDC datasets. If you want numbers rather than a benchmark run, use it and skip the training entirely.
• generative-design → alongside: TDC's oracles (GSK3B, JNK3, DRD2, QED, SA) are the standard scoring functions for de novo generation, and the usual way to overfit a generator.
• medchem → after: an oracle-optimised molecule is not a real molecule until it survives alerts.

The split is the experiment. A random split on molecular data reports a number that will not survive contact with prospective compounds, because congeneric series from one paper land on both sides. That is the single reason to use PyTDC's loaders rather than train_test_split.