A review of machine learning methods for imbalanced data challenges in chemistry
Jiang Jian, Chunhuan Zhang, Ke Lü, Nicole Hayes, Yueying Zhu, Huahai Qiu, Bengong Zhang, Tianshou Zhou, Guo‐Wei Wei
Abstract
physical models, large language models (LLMs), and advanced mathematics. The benefit of balanced data in new material design and production and the persistent challenges are discussed. Overall, this review aims to elucidate the prevalent ML techniques applied to mitigate the impacts of imbalanced data within the field of chemistry and offer insights into future directions for research and application.
Topics & Concepts
Machine learningComputer scienceArtificial intelligenceData scienceChemistryComputational Drug Discovery MethodsData-Driven Disease SurveillancePharmaceutical Quality and Counterfeiting