TITLE: Therapeutics Data Commons: Machine Learning Datasets and Tasks for Drug Discovery and Development 

ABSTRACT: Therapeutics machine learning is an emerging field with incredible
opportunities for innovatiaon and impact. However, advancement in this field
requires formulation of meaningful learning tasks and careful curation of
datasets. Here, we introduce Therapeutics Data Commons (TDC), the first
unifying platform to systematically access and evaluate machine learning across
the entire range of therapeutics. To date, TDC includes 66 AI-ready datasets
spread across 22 learning tasks and spanning the discovery and development of
safe and effective medicines. TDC also provides an ecosystem of tools and
community resources, including 33 data functions and types of meaningful data
splits, 23 strategies for systematic model evaluation, 17 molecule generation
oracles, and 29 public leaderboards. All resources are integrated and
accessible via an open Python library. We carry out extensive experiments on
selected datasets, demonstrating that even the strongest algorithms fall short
of solving key therapeutics challenges, including real dataset distributional
shifts, multi-scale modeling of heterogeneous data, and robust generalization
to novel data points. 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 is an open-science initiative available at
https://tdcommons.ai.
Therapeutics machine learning is an emerging field with incredible
opportunities for innovatiaon and impact. However, advancement in this field
requires formulation of meaningful learning tasks and careful curation of
datasets. Here, we introduce Therapeutics Data Commons (TDC), the first
unifying platform to systematically access and evaluate machine learning across
the entire range of therapeutics. To date, TDC includes 66 AI-ready datasets
spread across 22 learning tasks and spanning the discovery and development of
safe and effective medicines. TDC also provides an ecosystem of tools and
community resources, including 33 data functions and types of meaningful data
splits, 23 strategies for systematic model evaluation, 17 molecule generation
oracles, and 29 public leaderboards. All resources are integrated and
accessible via an open Python library. We carry out extensive experiments on
selected datasets, demonstrating that even the strongest algorithms fall short
of solving key therapeutics challenges, including real dataset distributional
shifts, multi-scale modeling of heterogeneous data, and robust generalization
to novel data points. 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 is an open-science initiative available at
https://tdcommons.ai.
Therapeutics Data Commons: Machine Learning Datasets and Tasks for Drug Discovery and Development
http://arxiv.org/abs/2102.09548v2