Therapeutics Data Commons

Machine Learning Datasets and Tasks for Therapeutics

Therapeutics Data Commons is an open-science platform with AI/ML-ready datasets and learning tasks for therapeutics, spanning the discovery and development of safe and effective medicines. TDC also provides an ecosystem of tools, libraries, leaderboards, and community resources, including data functions, strategies for systematic model evaluation, meaningful data splits, data processors, and molecule generation oracles. All resources are integrated and accessible via an open Python library.
Our Vision
Therapeutics machine learning is an exciting field with incredible opportunities for expansion, innovation, and impact. The collection of curated datasets, learning tasks, and benchmarks in Therapeutics Data Commons (TDC) serves as a meeting point for domain and machine learning scientists. TDC is the first unifying framework to systematically access and evaluate machine learning across the entire range of therapeutics. We envision that TDC can facilitate algorithmic and scientific advances and considerably accelerate machine-learning model development, validation and transition into biomedical and clinical implementation.
TDC at a Glance
TDC at a glance
TDC is a community-driven and open-science initiative. If you want to contribute to TDC, join us on Slack.

Intuitive Interface

TDC software is minimally dependent on external packages. Any TDC dataset can be retrieved using only 3 lines of code.

From Bench to Bedside

TDC covers a wide range of learning tasks, including target discovery, activity screening, efficacy, safety, and manufacturing across biomedical products, including small molecules, antibodies, and vaccines.

Numerous Data Functions

TDC provides extensive data functions, including data evaluators, meaningful data splits, data processors, and molecule generation oracles.

Loading a dataset in TDC:

from tdc.single_pred import ADME
data = ADME(name='Caco2_Wang')
df = data.get_data()
split = data.get_split()