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

ImDrug: A Benchmark for Deep Imbalanced Learning in AI-aided Drug Discovery