The use of chemical oxidants including ozone, chlorine, and chlorine dioxide in water treatment processes is essential to mitigate waterborne diseases and abate inorganic and organic micropollutants. Chemical oxidants have been used successfully for decades for the provision of safe drinking water and, more recently, for wastewater treatment. Their reactions with matrix components and organic pollutants create transformation and disinfection (by)products which can cause adverse effects on human and environmental health. Providing efficient disinfection/oxidation while minimizing toxic product formation is particularly challenging for wastewater treatment and potable water reuse purposes where concentrations of dissolved organic matter and organic pollutants are typically much faster. Evaluating the efficacy of pollutant abatement in chemical oxidation processes and identifying potentially hazardous transformation products are thus paramount for the implementation of adequate water treatment strategies. To date, there is a large data base on reactions and mechanisms of oxidation reactions, however, comprehensive experimental descriptions of all reaction pathways pertinent to oxidative water treatment would be extremely laborious and experimental evidence remains inherently incomplete. Computational support is therefore required to complement the experimental characterizations of oxidation processes. In fact, chemoinformatic (i.e., datadriven) pathway prediction tools and first-principles quantum chemical calculations have already assisted the experimental elucidation of kinetics and reaction pathways. Previously, empirical reaction rules for reactive functional groups of compounds with unknown reactivity towards oxidants were applied to predict the formation of transformation products. Furthermore, quantum chemical descriptors for structure activity relationships were established, allowing access to chemical structures, their Gibbs energies, and barrier heights for the characterization of reaction pathways. However, quantum chemical construction of potential energy surfaces (PES) requires significant manual input and guidance, limiting computational analyses to specific reaction coordinates. These circumstances have so far prevented a more expansive, systematic, and possibly predictive evaluation of reactions involving chemical oxidants and organic micropollutants.