alterlab-pytdc

PyTDC (Therapeutics Data Commons)

Overview

PyTDC is an open-science platform providing AI-ready datasets and benchmarks for drug discovery and development. Access curated datasets spanning the entire therapeutics pipeline with standardized evaluation metrics and meaningful data splits, organized into three categories: single-instance prediction (molecular/protein properties), multi-instance prediction (drug-target interactions, DDI), and generation (molecule generation, retrosynthesis).

When to Use This Skill

This skill should be used when:

• Working with drug discovery or therapeutic ML datasets
• Benchmarking machine learning models on standardized pharmaceutical tasks
• Predicting molecular properties (ADME, toxicity, bioactivity)
• Predicting drug-target or drug-drug interactions
• Generating novel molecules with desired properties
• Accessing curated datasets with proper train/test splits (scaffold, cold-split)
• Using molecular oracles for property optimization

Installation & Setup

Install PyTDC using pip:

uv pip install PyTDC

To upgrade to the latest version:

uv pip install PyTDC --upgrade

Core dependencies (automatically installed):

• numpy, pandas, tqdm, seaborn, scikit_learn, fuzzywuzzy

Additional packages are installed automatically as needed for specific features.

Quick Start

The basic pattern for accessing any TDC dataset follows this structure:

from tdc.<problem> import <Task>
data = <Task>(name='<Dataset>')
split = data.get_split(method='scaffold', seed=1, frac=[0.7, 0.1, 0.2])
df = data.get_data(format='df')

Where:

• <problem>: One of single_pred, multi_pred, or generation
• <Task>: Specific task category (e.g., ADME, DTI, MolGen)
• <Dataset>: Dataset name within that task

Example - Loading ADME data:

from tdc.single_pred import ADME
data = ADME(name='Caco2_Wang')
split = data.get_split(method='scaffold')
# Returns dict with 'train', 'valid', 'test' DataFrames