In this chapter, we present the Activated Sludge Model for Xenobiotics (ASM-X), which offers the largest systematic and consistent database to predict illicit drug and pharmaceutical biomarker fate in wastewater conveyed in sewer pipes. ASM-X was originally developed for predicting the removal of trace organic pharmaceutical chemical pollutants (micropollutants), notably antibiotics in biological wastewater treatment. Here, we present the identification of simulation models in the ASM-X framework for illicit biomarker transformation, physicochemical partitioning onto particulate matter, and diffusive transport in biofilms. Model parameters were estimated using experimental data obtained with in-sewer biocatalytic environments represented by suspended solids and biofilm. A systematic methodology for inferring reliable estimates of unique parameter sets in tandem with chemical transformation pathways was developed using Bayesian optimization. This is a method that can be generalized to any other chemodynamics problems focusing on quantifying chemical biotransformation using external prior metabolic information. The method developed can offer a platform to promote a more effective interaction between analytical chemists and modelers to develop smart experimental designs conducive to effective model development. Additionally, the identification method developed can be used in conjunction with optimal experimental designs to effectively identify model structures and parameters.