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

DrugOOD: Out-of-Distribution (OOD) Dataset Curator and Benchmark for  AI-aided Drug Discovery -- A Focus on Affinity Prediction Problems with Noise  Annotations