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=Volume 12 - 2021

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 new Salmonella
mutagenicity dataset with 8290 unique chemical structures utilizing
MolPrint2D and Chemistry Development Kit (CDK) descriptors.  Crossvalidation
accuracies of all investigated models ranged from 80-85% 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.