  The last decade has witnessed a prosperous development of computational
methods and dataset curation for AI-aided drug discovery (AIDD). However,
real-world pharmaceutical datasets often exhibit highly imbalanced
distribution, which is overlooked by the current literature but may severely
compromise the fairness and generalization of machine learning applications.
Motivated by this observation, we introduce ImDrug, a comprehensive benchmark
with an open-source Python library which consists of 4 imbalance settings, 11
AI-ready datasets, 54 learning tasks and 16 baseline algorithms tailored for
imbalanced learning. It provides an accessible and customizable testbed for
problems and solutions spanning a broad spectrum of the drug discovery pipeline
such as molecular modeling, drug-target interaction and retrosynthesis. We
conduct extensive empirical studies with novel evaluation metrics, to
demonstrate that the existing algorithms fall short of solving medicinal and
pharmaceutical challenges in the data imbalance scenario. We believe that
ImDrug opens up avenues for future research and development, on real-world
challenges at the intersection of AIDD and deep imbalanced learning.
