PyTDC datasets and data access

This reference targets the PyTDC 1.1.15 source distribution, verified 2026-07-23. Dataset registries evolve independently of this skill, so query the installed package instead of copying a historical catalog.

Discovery is not download

tdc.metadata contains static Python registries. Reading them does not construct a loader, contact Harvard Dataverse, or download a dataset:

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

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

The package helper is also metadata-only:

from tdc.utils import retrieve_dataset_names

# Task key is exact and case-sensitive in PyTDC 1.1.15.
names = retrieve_dataset_names("ADME")

retrieve_dataset_names returns normalized package identifiers, usually lowercase. Pass an exact returned name to scripts. Although constructors support fuzzy matching, fuzzy selection can hide a typo or select an unintended resource.

Public task imports in 1.1.15

These names come from the stable source distribution's public __init__.py files, not from older prose catalogs.

Single-instance prediction

from tdc.single_pred import (
    ADME,
    CRISPROutcome,
    Develop,
    Epitope,
    HTS,
    Paratope,
    QM,
    Tox,
    Yields,
)

Multi-instance prediction

from tdc.multi_pred import (
    AntibodyAff,
    Catalyst,
    DDI,
    DrugRes,
    DrugSyn,
    DTI,
    GDA,
    MTI,
    PeptideMHC,
    PPI,
    PerturbOutcome,
    ProteinPeptide,
    TCREpitopeBinding,
    TrialOutcome,
)

Some new task classes and resource APIs do not share the ordinary download-to-DataFrame contract. Confirm that a task appears both in public imports and metadata.dataset_names before using the generic loader CLI.

Generation

from tdc.generation import MolGen, Reaction, RetroSyn, SBDD

The 1.1.15 registry exposes ordinary MolGen corpora under MolGen, paired reaction data under Reaction/RetroSyn, and structure-based resources under the lowercase sbdd key. The bundled generic loader supports the verified ordinary MolGen/Reaction/RetroSyn paths; use specialized upstream documentation for SBDD.

What causes a download

Ordinary constructor calls immediately invoke a load wrapper:

from tdc.single_pred import ADME

# Potential network and disk write if the exact local file is absent.
data = ADME(name="caco2_wang", path=".pytdc-data")

For core datasets, PyTDC 1.1.15:

1. normalizes the requested name against the task registry;
2. checks for a task-specific filename beneath path;
3. if absent, requests a Harvard Dataverse file endpoint;
4. streams the response to that path;
5. parses the local tab/CSV/XLSX/pickle/JSON/H5AD/archive format.

Many datasets are associated with the Harvard Dataverse collection https://doi.org/10.7910/DVN/21LKWG. Newer resources may instead use CELLxGENE, Hugging Face, or task-specific APIs; inspect the resource class before approval.

PyTDC checks for expected filenames, not a complete content-addressed cache with documented checksums. An interrupted or stale local file may therefore need manual review. Never delete or redownload a user's cache without confirmation.

Cache locations

• Ordinary loader default: ./data
• MolGen/Reaction/RetroSyn default: ./data
• Generic BenchmarkGroup default: ./data, then a normalized group subdirectory
• Download-backed oracle default: ./oracle
• Bundled dataset CLI default: .pytdc-data
• Bundled benchmark CLI default: .pytdc-benchmarks
• Bundled MolGen CLI default: .pytdc-molgen
• Bundled oracle runtime default: .pytdc-oracles (upstream creates oracle/ inside it for acknowledged checkpoints)

All bundled CLIs require relative paths inside the current workspace. They do not overwrite JSON outputs unless --force is supplied.

Audit an existing directory without network access:

python scripts/cache_audit.py --cache-dir .pytdc-data --largest 20

The audit reports regular-file counts, total bytes, extensions, bounded largest files, errors, and skipped symbolic links. It does not hash, modify, or upload data.

Approval gate

Before constructing any loader, present:

• exact package version, task, and dataset registry name;
• official TDC task/dataset page and original data source;
• dataset-specific license/terms and required citations;
• published row/record count or archive size when available;
• proposed relative cache path and available local disk;
• likely network transfer and decompressed footprint;
• split method, fractions, seed, and rationale;
• any sensitive/proprietary inputs that must not leave the environment.

Ask for explicit approval before the first download or any large redownload. The bundled scripts make planning the default and reserve construction for --execute; MolGen additionally requires --download.

Data license is not the code license

The mims-harvard/TDC codebase and this skill are MIT-licensed. That does not grant a blanket MIT license for hosted data.

Official task pages show dataset-specific entries. As of the verification date, examples include Creative Commons licenses, “Not Specified” entries, and non-commercial terms (for example, clinical-trial outcome data). Treat the exact page and original provider terms as authoritative for:

• commercial use;
• redistribution or derivative datasets;
• attribution and citation;
• patient/clinical restrictions;
• access tokens or API terms;
• geographic or institutional restrictions.

If the TDC page says “Not Specified,” do not infer permission from its nearby Creative Commons link. Trace the original source and ask the user to resolve the license before reuse.

Returned data

For ordinary prediction loaders:

frame = data.get_data(format="df")
mapping = data.get_data(format="dict")

Supported formats and columns are loader-specific. Common prediction frames use entity identifiers/representations plus Y, but do not hard-code Drug, Target, or identifier columns before inspecting frame.columns.

Some loaders also expose format="DeepPurpose". Do not assume PyG, DGL, or arbitrary graph formats are valid get_data formats; representation conversion is a separate, dependency-heavy workflow.

For multi-label datasets, constructors can require label_name. Discover labels without loading the main dataset:

from tdc.utils import retrieve_label_name_list

labels = retrieve_label_name_list("tox21")

Label meaning may require a separate mapping file and therefore can trigger its own download. Do not call it during a metadata-only plan.

Dataset and split provenance

Record at minimum:

{
  "package": "PyTDC",
  "version": "1.1.15",
  "task": "ADME",
  "dataset": "caco2_wang",
  "cache_path": ".pytdc-data",
  "split_method": "scaffold",
  "split_seed": 42,
  "split_fractions": [0.7, 0.1, 0.2],
  "license_reviewed": true,
  "source_page": "https://tdcommons.ai/single_pred_tasks/adme"
}

Also record the downloaded filename, byte size, retrieval date, row count, columns, target transformation, duplicate handling, missing-value handling, and split overlap audits. Do not claim a split is leakage-free solely because it is named scaffold or cold_split.

Stable verified examples

These are used only as API checks; run package discovery before use:

• ADME → caco2_wang (official ADME page)
• DTI → davis and bindingdb_patent (official DTI/benchmark sources)
• MolGen → moses (official molecule-generation page)

Names such as PairMolGen, generic Prodrug, or arbitrary GuacaMol datasets do not appear in the PyTDC 1.1.15 public generation imports/registry and must not be presented as supported loaders.