ImDrug: A Benchmark for Deep Imbalanced Learning in AI-aided Drug Discovery
This work addresses data imbalance issues in pharmaceutical applications, providing a benchmark for future research, but it is incremental as it builds on existing imbalanced learning methods.
The authors tackled the problem of imbalanced data in AI-aided drug discovery by introducing ImDrug, a benchmark with 11 datasets and 54 tasks, showing that existing algorithms fail to address these challenges effectively.
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.