A Comparison of Nine Machine Learning Mutagenicity Models and Their Application for Predicting Pyrrolizidine Alkaloids
Christoph Helma, Verena Schöning, Jürgen Drewe, Philipp Boss
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.
Topics & Concepts
PyrrolizidineRandom forestSupport vector machineSalmonellaLogistic regressionArtificial intelligenceChemistryMathematicsBiologyStereochemistryStatisticsComputer scienceGeneticsBacteriaPlant Toxicity and Pharmacological PropertiesBotanical Research and ChemistryDrug-Induced Hepatotoxicity and Protection