  AI-aided drug discovery (AIDD) is gaining increasing popularity due to its
promise of making the search for new pharmaceuticals quicker, cheaper and more
efficient. In spite of its extensive use in many fields, such as ADMET
prediction, virtual screening, protein folding and generative chemistry, little
has been explored in terms of the out-of-distribution (OOD) learning problem
with \emph{noise}, which is inevitable in real world AIDD applications.
  In this work, we present DrugOOD, a systematic OOD dataset curator and
benchmark for AI-aided drug discovery, which comes with an open-source Python
package that fully automates the data curation and OOD benchmarking processes.
We focus on one of the most crucial problems in AIDD: drug target binding
affinity prediction, which involves both macromolecule (protein target) and
small-molecule (drug compound). In contrast to only providing fixed datasets,
DrugOOD offers automated dataset curator with user-friendly customization
scripts, rich domain annotations aligned with biochemistry knowledge, realistic
noise annotations and rigorous benchmarking of state-of-the-art OOD algorithms.
Since the molecular data is often modeled as irregular graphs using graph
neural network (GNN) backbones, DrugOOD also serves as a valuable testbed for
\emph{graph OOD learning} problems. Extensive empirical studies have shown a
significant performance gap between in-distribution and out-of-distribution
experiments, which highlights the need to develop better schemes that can allow
for OOD generalization under noise for AIDD.
