Machine learning predictive classification models for the carcinogenic activity of activated metabolites derived from aromatic amines and nitroaromatics.

A 3D-QSAR study based on DFT descriptors and machine learning calculations is presented in this work. Our goal has been to build predictive models for classifying the carcinogenic activity of a set of aromatic amines (AA) and nitroaromatic (NA) compounds. As the main result, we stress that calculations must consider both the activated metabolites (derived from AA and NA species) and the water solvent to obtain reliable predictive classification models.