Explainable No-Code OECD-Compliant Machine Learning Models to Predict the Mutagenic Activity of Polycyclic Aromatic Hydrocarbons and Their Radical Cation Metabolites

Supplementary Material for "Explainable No-Code OECD-Compliant Machine Learning Models to Predict the Mutagenic Activity of Polycyclic Aromatic Hydrocarbons and Their Radical Cation Metabolites" (Submitted) : All supporting files necessary for this research are combined in a single .ZIP archive. The archive includes two primary folders: 'Outputs' and 'WEKA File', holding .xyz files for the molecular structures of the activated metabolites and procarcinogens, respectively. The 'WEKA File’ folder also provides the .arff files used for training and testing, organized by each specific data split. The ‘PAHs-GFN2-Data.xlsx' file includes the primary datasets used in this study, including all extracted molecular details, SMILES notations, numerical IDs, CDFT descriptors, and biological responses, categorized by data split.