A Comparison of Nine Machine Learning Mutagenicity Models and Their Application for Predicting Pyrrolizidine Alkaloids

Random forest, support vector machine, logistic regression, neural networks and k-nearest neighbor (lazar) algorithms, were applied to a new Salmonella mutagenicity dataset with 8,290 unique chemical structures utilizing MolPrint2D Chemistry Development Kit (CDK) descriptors. Crossvalidation accuracies of all investigated models ranged from 80 85% which is comparable the interlaboratory variability assay. Pyrrolizidine alkaloid predictions showed clear distinction between groups, where otonecines had highest proportion positive monoesters lowest.