  Machine learning for therapeutics is an emerging field with incredible
opportunities for innovation and expansion. Despite the initial success, many
key challenges remain open. Here, we introduce Therapeutics Data Commons (TDC),
the first unifying framework to systematically access and evaluate machine
learning across the entire range of therapeutics. At its core, TDC is a
collection of curated datasets and learning tasks that can translate
algorithmic innovation into biomedical and clinical implementation. To date,
TDC includes 66 machine learning-ready datasets from 22 learning tasks,
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 datasets and learning tasks are integrated and accessible via an
open-source library. We envision that TDC can facilitate algorithmic and
scientific advances and accelerate development, validation, and transition into
production and clinical implementation. TDC is a continuous, open-source
initiative, and we invite contributions from the research community. TDC is
publicly available at https://tdcommons.ai.
