Another approach that has been employed is the modeling of individual SOM functional groups . This reduces the complexity of the systems, which in turn enables the application of more accurate methods, such as those based on quantum mechanics. However, this approach could be compromised by the number of functional groups that have to be considered to sufficiently capture the characteristics of SOM. Ahmed and colleagues addressed this challenge by utilizing a large test set comprising over 30 separate and distinct organic molecules that represent the most common SOM functional groups. These molecules were identified through extensive characterization and analysis of numerous soil samples using pyrolysis field-ionization mass spectrometry (Py-FIMS) and x-ray absorption near-edge spectroscopy (XANES) . This approach involved simulating various SOM building blocks, including carbohydrates, phenols/lignin monomers, lignin dimers, lipids, alkylaromatics, non-amide nitrogen-containing compounds, amides, and sterols. It effectively captured the binding/interactions between organic contaminants and SOM on three levels: individual SOM functional groups (small organic molecules); SOM compound classes (or building blocks); and SOM as a whole. By applying quantum mechanical calculations and quantitative structure-activity relationship (QSAR) models, Ahmed and colleagues identified key parameters driving the specific binding mechanism. Overall, this molecular modeling approach showed good agreement with experimental results and provided insights into molecular-level interactions that are beyond the reach of current experimental methods.