Quantitative Structure–Activity Relationships for Predicting Organic Contaminant Reactivity in Electrochemical Advanced Oxidation Processes: Descriptors, Kinetics, and Machine Learning Frontiers

Electrochemical advanced oxidation processes (EAOPs) have become a prominent tool in wastewater remediation of refractory organic pollutants due to their versatility and ability to generate powerful, in situ oxidant species—predominantly hydroxyl radicals (•OH). Concurrently, quantitative structure–activity relationship (QSAR) models have emerged as vital tools for predicting activity in high‐priority areas like drug development and toxicity. In this review, we address the application of QSARs in decoding reactivity within electrooxidation processes. We trace their evolution from early origins using the Hammett parameter () as a molecular descriptor to quantum chemical descriptors—such as , ionization potentials (IPs), and Fukui functions—and finally to advanced machine learning (ML) approaches like graph neural networks (GNNs) designed to navigate complex real wastewater matrices. Additionally, we highlight the anode as a core mechanistic variable that dictates oxidant selectivity. Finally, we discuss the primary limitations hindering ML adoption in EAOPs—including small training sets, a lack of electrochemical‐specific datasets, limited water‐matrix transferability, black‐box interpretability, and conflicting optimization metrics—and explore emerging strategies to overcome them, such as transfer learning, explainable artificial intelligence (XAI), and physics‐informed ML.