AUTHOR=Helma Christoph , Schöning Verena , Drewe Jürgen , Boss Philipp TITLE=A Comparison of Nine Machine Learning Mutagenicity Models and Their Application for Predicting Pyrrolizidine Alkaloids JOURNAL=Frontiers in Pharmacology VOLUME=12 YEAR=2021 URL=https://www.frontiersin.org/journals/pharmacology/articles/10.3389/fphar.2021.708050 DOI=10.3389/fphar.2021.708050 ISSN=1663-9812 ABSTRACT=

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 and Chemistry Development Kit (CDK) descriptors. Crossvalidation accuracies of all investigated models ranged from 80 to 85% which is comparable with the interlaboratory variability of the Salmonella mutagenicity assay. Pyrrolizidine alkaloid predictions showed a clear distinction between chemical groups, where otonecines had the highest proportion of positive mutagenicity predictions and monoesters the lowest.