PyTDC (Therapeutics Data Commons)

Overview

PyTDC is an open-science platform providing AI-ready datasets and benchmarks for drug discovery. It organizes therapeutics data into three categories: single-instance prediction (molecular/protein properties), multi-instance prediction (drug-target interactions), and generation (molecule design, retrosynthesis). All datasets come with standardized splits, evaluation metrics, and molecular oracles.

When to Use

• Loading curated ADME, toxicity, or bioactivity datasets for ML model training
• Benchmarking drug discovery models with standardized 5-seed evaluation protocols
• Predicting drug-target or drug-drug interactions with proper cold-split evaluation
• Generating novel molecules and scoring them with molecular oracles (QED, SA, DRD2, GSK3B)
• Accessing scaffold-based or temporal train/test splits for pharmaceutical ML
• Converting molecular representations (SMILES to PyG graphs, ECFP fingerprints, SELFIES)
• For chemical database queries (compound search, bioactivity), use chembl-database-bioactivity instead
• For molecular featurization beyond format conversion, use molfeat instead

Prerequisites

uv pip install PyTDC
# Core deps: numpy, pandas, scikit-learn, tqdm, fuzzywuzzy
# Optional: rdkit (scaffold splits), torch-geometric (PyG conversion)

API Note: TDC downloads datasets on first access (~10-500 MB per dataset). Specify path='data/' to control download location. No API key required.

Quick Start

from tdc.single_pred import ADME
from tdc import Evaluator

# Load dataset with scaffold split
data = ADME(name='Caco2_Wang')
split = data.get_split(method='scaffold', seed=42, frac=[0.7, 0.1, 0.2])
train, valid, test = split['train'], split['valid'], split['test']
print(f"Train: {len(train)}, Valid: {len(valid)}, Test: {len(test)}")
# Train: ~640, Valid: ~91, Test: ~182

# Evaluate predictions
evaluator = Evaluator(name='MAE')
# score = evaluator(test['Y'].values, predictions)

Core API

Module 1: Single-Instance Prediction — Dataset Access

Load datasets for predicting properties of individual molecules or proteins.

from tdc.single_pred import ADME, Tox, HTS, QM

# ADME — pharmacokinetic properties
data = ADME(name='Caco2_Wang')       # Intestinal permeability (regression)
data = ADME(name='BBB_Martins')       # Blood-brain barrier (binary)
data = ADME(name='Lipophilicity_AstraZeneca')  # LogD (regression)
data = ADME(name='Solubility_AqSolDB')         # Aqueous solubility

# Toxicity — adverse effects
data = Tox(name='hERG')              # Cardiotoxicity (binary)
data = Tox(name='AMES')              # Mutagenicity (binary)
data = Tox(name='DILI')              # Drug-induced liver injury
data = Tox(name='ClinTox')           # Clinical trial toxicity

# Access data as DataFrame
df = data.get_data(format='df')
print(df.columns.tolist())
# ['Drug_ID', 'Drug', 'Y'] — Drug is SMILES, Y is target label
print(f"Dataset size: {len(df)}, Label range: [{df['Y'].min():.2f}, {df['Y'].max():.2f}]")

Other single-prediction tasks: HTS (screening), QM (quantum mechanics), Yields, Epitope, Develop, CRISPROutcome.

Module 2: Multi-Instance Prediction — Interaction Datasets

Load datasets for predicting interactions between pairs of biomedical entities.

from tdc.multi_pred import DTI, DDI, PPI

# Drug-Target Interaction — binding affinity
data = DTI(name='BindingDB_Kd')      # 52,284 pairs, Kd values
data = DTI(name='DAVIS')             # 30,056 pairs, kinase binding
data = DTI(name='KIBA')              # 118,254 pairs, kinase bioactivity

# Drug-Drug Interaction — interaction type prediction
data = DDI(name='DrugBank')           # 191,808 pairs, 86 interaction types

# Protein-Protein Interaction
data = PPI(name='HuRI')

# Multi-instance data format
df = data.get_data(format='df')
print(df.columns.tolist())
# ['Drug_ID', 'Drug', 'Target_ID', 'Target', 'Y']
# Drug=SMILES, Target=protein sequence, Y=binding affinity or class

Other multi-instance tasks: GDA, DrugRes, DrugSyn, PeptideMHC, AntibodyAff, MTI, Catalyst, TrialOutcome.

Module 3: Generation Tasks — Molecular Design

Load training sets and oracles for molecule generation and retrosynthesis.

from tdc.generation import MolGen, RetroSyn, PairMolGen
from tdc import Oracle

# Molecule generation — training data
data = MolGen(name='ChEMBL_V29')     # 1.6M drug-like SMILES
split = data.get_split()
train_smiles = split['train']['Drug'].tolist()

# Oracle scoring — evaluate generated molecules
oracle = Oracle(name='GSK3B')         # GSK3B inhibition predictor (0-1)
score = oracle('CC(C)Cc1ccc(cc1)C(C)C(O)=O')
print(f"GSK3B score: {score:.4f}")

# Batch evaluation
scores = oracle(['CCO', 'c1ccccc1', 'CC(=O)O'])
print(f"Batch scores: {scores}")

# Retrosynthesis — reaction prediction
data = RetroSyn(name='USPTO')         # 1.9M reactions
split = data.get_split()

# Paired generation — prodrug design
data = PairMolGen(name='Prodrug')

Module 4: Data Splits and Evaluation

Apply meaningful data splits and standardized evaluation metrics.

from tdc.single_pred import ADME
from tdc.multi_pred import DTI
from tdc import Evaluator

# Scaffold split — ensures chemical diversity between sets
data = ADME(name='Caco2_Wang')
split = data.get_split(method='scaffold', seed=42, frac=[0.7, 0.1, 0.2])

# Cold splits — for DTI (unseen drugs/targets in test set)
data = DTI(name='BindingDB_Kd')
cold_drug = data.get_split(method='cold_drug', seed=1)
cold_target = data.get_split(method='cold_target', seed=1)

# Verify no overlap in cold split
train_drugs = set(cold_drug['train']['Drug_ID'])
test_drugs = set(cold_drug['test']['Drug_ID'])
print(f"Drug overlap: {len(train_drugs & test_drugs)}")  # 0

# Evaluation metrics
eval_mae = Evaluator(name='MAE')
eval_auc = Evaluator(name='ROC-AUC')
eval_spearman = Evaluator(name='Spearman')
# score = eval_mae(y_true, y_pred)

Available split methods: random, scaffold (Bemis-Murcko), cold_drug, cold_target, cold_drug_target, temporal.

Available metrics: Classification — ROC-AUC, PR-AUC, F1, Accuracy, Kappa. Regression — RMSE, MAE, R2, MSE. Ranking — Spearman, Pearson. Multi-label — Micro-F1, Macro-F1.

Module 5: Benchmark Groups

Run standardized multi-seed evaluation protocols for model comparison.

from tdc.benchmark_group import admet_group

# Load ADMET benchmark (22 datasets)
group = admet_group(path='data/')

# Standard 5-seed evaluation protocol
benchmark = group.get('Caco2_Wang')
predictions = {}

for seed in [1, 2, 3, 4, 5]:
    train_df = benchmark['train']
    valid_df = benchmark['valid']
    test_df = benchmark['test']
    # Train your model on train_df, tune on valid_df
    # predictions[seed] = model.predict(test_df['Drug'])
    predictions[seed] = test_df['Y'].values  # placeholder

# Get benchmark results
results = group.evaluate(predictions)
print(f"Mean MAE: {results['Caco2_Wang'][0]:.4f} ± {results['Caco2_Wang'][1]:.4f}")

Key Concepts

Dataset Organization

Category	Import Path	Task Examples	Data Format
Single-Instance	tdc.single_pred	ADME, Tox, HTS, QM	Drug (SMILES) + Y (label)
Multi-Instance	tdc.multi_pred	DTI, DDI, PPI, DrugSyn	Drug + Target + Y
Generation	tdc.generation	MolGen, RetroSyn	SMILES collections
Benchmark	tdc.benchmark_group	admet_group	Curated splits

Oracle Categories

Category	Examples	Speed	Output Range
Biochemical	DRD2, GSK3B, JNK3, 5HT2A	Medium (ML)	0-1 probability
Physicochemical	QED, SA, LogP, MW	Fast (rule-based)	Varies by metric
Composite	Isomer_Meta, Median1/2, Rediscovery	Medium	0-1 combined
Specialized	ASKCOS, Docking, Vina	Slow (external)	Varies

Data Processing Utilities

Utility	Function	Example
Format conversion	MolConvert(src, dst)	SMILES → PyG, ECFP, SELFIES, DGL
Molecule filters	MolFilter(filters)	PAINS, BMS, Glaxo, drug-likeness
Label binarization	label_transform()	Continuous → binary at threshold
Unit conversion	label_transform(from_unit, to_unit)	nM → pIC50
ID resolution	cid2smiles(), uniprot2seq()	PubChem CID → SMILES
Dataset listing	retrieve_dataset_names(task)	List all ADME datasets

Common Workflows

Workflow 1: Multi-Seed ADME Model Evaluation

from tdc.single_pred import ADME
from tdc import Evaluator
import numpy as np

data = ADME(name='Caco2_Wang')
evaluator = Evaluator(name='MAE')

results = []
for seed in [1, 2, 3, 4, 5]:
    split = data.get_split(method='scaffold', seed=seed)
    train, valid, test = split['train'], split['valid'], split['test']
    # model.fit(train['Drug'], train['Y'])
    # preds = model.predict(test['Drug'])
    preds = test['Y'].values + np.random.normal(0, 0.1, len(test))  # placeholder
    score = evaluator(test['Y'].values, preds)
    results.append(score)
    print(f"Seed {seed}: MAE = {score:.4f}")

print(f"Mean MAE: {np.mean(results):.4f} ± {np.std(results):.4f}")

Workflow 2: Multi-Objective Molecular Scoring

from tdc import Oracle
import numpy as np

# Define multi-objective scoring
oracles = {
    'QED': (Oracle(name='QED'), 0.3),      # drug-likeness
    'SA': (Oracle(name='SA'), 0.3),         # synthetic accessibility
    'GSK3B': (Oracle(name='GSK3B'), 0.4),   # target activity
}

test_smiles = ['CC(C)Cc1ccc(cc1)C(C)C(O)=O', 'c1ccc2c(c1)cc1ccc3cccc4ccc2c1c34']

for smi in test_smiles:
    scores = {}
    weighted_sum = 0
    for name, (oracle, weight) in oracles.items():
        score = oracle(smi)
        scores[name] = score
        weighted_sum += score * weight
    print(f"SMILES: {smi[:30]}...")
    print(f"  Scores: {scores}")
    print(f"  Weighted: {weighted_sum:.4f}")

Workflow 3: Cold-Split DTI Evaluation (text-only)

1. Load DTI dataset — DTI(name='BindingDB_Kd') (Core API Module 2)
2. Apply cold_drug split — data.get_split(method='cold_drug', seed=seed) (Core API Module 4)
3. Verify zero drug overlap between train and test sets (Module 4 overlap check)
4. Train model on train set, predict on test set
5. Evaluate with Spearman correlation — Evaluator(name='Spearman') (Module 4)
6. Repeat for 5 seeds and report mean ± std (same pattern as Workflow 1)

Key Parameters

Parameter	Function/Module	Default	Range/Options	Effect
method	get_split()	'scaffold'	random, scaffold, cold_drug, cold_target, temporal	Split strategy for train/test
seed	get_split()	42	1-5 for benchmarks	Reproducibility; use 5 seeds for benchmarks
frac	get_split()	[0.7, 0.1, 0.2]	Sum must equal 1.0	Train/valid/test proportions
name	Evaluator()	—	MAE, RMSE, ROC-AUC, Spearman, etc.	Evaluation metric
name	Oracle()	—	QED, SA, GSK3B, DRD2, etc.	Scoring function for molecules
src/dst	MolConvert()	—	SMILES, SELFIES, PyG, DGL, ECFP4	Molecular representation formats
path	admet_group()	'data/'	Any directory	Dataset download/cache location
format	get_data()	'df'	'df', 'dict'	Output data format

Best Practices

1. Always use scaffold splits for molecular property prediction — random splits leak structural information and inflate performance metrics
2. Report 5-seed evaluations with mean ± std — single-seed results are unreliable for method comparison
3. Use cold splits for interaction prediction — cold_drug tests generalization to unseen drugs, cold_target to unseen targets
4. Filter molecules early with MolFilter (PAINS, drug-likeness) before training to remove problematic compounds
5. Normalize oracles appropriately — QED returns 0-1, SA returns 1-10 (lower is better), binding scores vary. Check oracle documentation before combining
6. Cache datasets by specifying a persistent path — avoids re-downloading large datasets across sessions

Common Recipes

Recipe 1: Dataset Exploration and Statistics

from tdc.single_pred import ADME
from tdc.utils import retrieve_dataset_names

# List all ADME datasets
datasets = retrieve_dataset_names('ADME')
print(f"Available ADME datasets: {datasets}")

# Load and inspect
data = ADME(name='Caco2_Wang')
df = data.get_data(format='df')
print(f"Size: {len(df)}")
print(f"Label stats: mean={df['Y'].mean():.2f}, std={df['Y'].std():.2f}")
print(f"SMILES example: {df['Drug'].iloc[0]}")

Recipe 2: Molecule Format Conversion Pipeline

from tdc.chem_utils import MolConvert

# SMILES to multiple representations
smiles = 'CC(C)Cc1ccc(cc1)C(C)C(O)=O'

converter_ecfp = MolConvert(src='SMILES', dst='ECFP4')
converter_selfies = MolConvert(src='SMILES', dst='SELFIES')

ecfp = converter_ecfp(smiles)
selfies = converter_selfies(smiles)
print(f"ECFP4 shape: {ecfp.shape}")    # (1024,) binary fingerprint
print(f"SELFIES: {selfies}")

Recipe 3: Custom Oracle with Constraint Satisfaction

from tdc import Oracle

# Define property constraints
constraints = {
    'QED': (Oracle(name='QED'), 0.5, 1.0),       # min, max
    'SA': (Oracle(name='SA'), 1.0, 4.0),
    'LogP': (Oracle(name='LogP'), -0.5, 5.0),
}

def check_constraints(smiles):
    """Check if molecule satisfies all property constraints."""
    results = {}
    all_pass = True
    for name, (oracle, lo, hi) in constraints.items():
        score = oracle(smiles)
        passed = lo <= score <= hi
        results[name] = {'score': score, 'passed': passed}
        all_pass = all_pass and passed
    return all_pass, results

passed, details = check_constraints('CC(C)Cc1ccc(cc1)C(C)C(O)=O')
for name, info in details.items():
    status = "PASS" if info['passed'] else "FAIL"
    print(f"{name}: {info['score']:.3f} [{status}]")

Troubleshooting

Problem	Cause	Solution
ModuleNotFoundError: tdc	Package not installed	uv pip install PyTDC
Scaffold split fails	Missing RDKit dependency	uv pip install rdkit for scaffold decomposition
Dataset download timeout	Large dataset or slow connection	Set path='data/' for persistent cache; retry
KeyError on dataset name	Wrong name or task category	Use retrieve_dataset_names('ADME') to list valid names
Oracle returns NaN	Invalid SMILES or RDKit parse failure	Validate SMILES with RDKit MolFromSmiles() first
Cold split empty test set	Too few unique entities	Use frac=[0.7, 0.1, 0.2] with larger datasets
Benchmark evaluation error	Wrong prediction format	Pass dict with seeds as keys: {1: preds1, 2: preds2, ...}
Memory error on large dataset	Full dataset loaded to memory	Process in chunks or use smaller split fractions
PyG conversion fails	torch-geometric not installed	uv pip install torch-geometric for graph conversion

Bundled Resources

references/datasets_catalog.md

Covers: complete catalog of all TDC datasets organized by task category (single-instance, multi-instance, generation) with dataset names, sizes, label types, and data sources. Relocated inline: top ADME/Tox/DTI datasets with code examples consolidated into Core API Modules 1-2. Omitted: None — all dataset entries preserved in catalog format.

references/oracles_utilities.md

Covers: detailed oracle documentation (all 17+ oracles with parameters, speed tiers, output ranges, custom oracle template) and data processing utilities (format conversion targets, molecule filter types, label transformation, entity resolution). Consolidated from original oracles.md + utilities.md. Relocated inline: Quick Start oracle usage pattern, top evaluation metrics, split methods, MolConvert pattern → Core API Modules 3-4. Omitted: distribution learning KS-test example — niche statistical comparison; leaderboard submission guide — platform-specific.

Script Disposition

• load_and_split_data.py (215 lines): scaffold/cold split patterns → Core API Module 4; custom split fractions → Key Parameters; evaluation examples → Workflow 1. Thin wrappers around get_split() and Evaluator().
• benchmark_evaluation.py (328 lines): 5-seed protocol → Core API Module 5 + Workflow 1; multi-dataset evaluation → Workflow 1 pattern; leaderboard guide → omitted (platform-specific).
• molecular_generation.py (405 lines): single/batch oracle usage → Core API Module 3; multi-objective scoring → Workflow 2; constraint satisfaction → Recipe 3; distribution learning → omitted (niche).

Related Skills

• chembl-database-bioactivity — for querying ChEMBL compound/target/activity data directly
• molfeat — for advanced molecular featurization beyond TDC's built-in MolConvert
• rdkit-cheminformatics — for molecular manipulation, substructure search, descriptor calculation

References

• TDC Official Website: https://tdcommons.ai
• TDC Documentation: https://tdc.readthedocs.io
• GitHub Repository: https://github.com/mims-harvard/TDC
• Huang et al. "Therapeutics Data Commons" NeurIPS 2021 Datasets and Benchmarks