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.

Therapeutics Data Commons（TDC）是系统评价整个疗法范围内机器学习的第一个统一平台，包括66个AI-ready数据集、22个学习任务、33个数据函数、23种系统模型评估策略、17个分子生成神器和29个公共排行榜，所有资源都通过开放式Python库集成和访问。该平台有助于算法和科学的进步，可以大大加速机器学习模型的开发、验证和转化到生物医学和临床应用中。

治疗数据共享平台：用于药物发现和开发的机器学习数据集和任务