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 largely 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.

本文介绍了一个名为 ImDrug 的开源 Python 库，用于药物发现中数据不平衡问题的评估和基准测试，包括 4 种不平衡设置、11 个 AI-ready 数据集、54 个学习任务和 16 种针对不平衡学习的基线算法，并通过实证研究和新的评估指标，证明了现有算法在处理实际药物发现中的数据不平衡问题时表现不佳，为 AIDD 和深度不平衡学习的研究和发展开辟了新的道路。

ImDrug: AI辅助药物发现中的深度不平衡学习基准