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

Frontiers in Pharmacology

Philipp Boss¹  Verena Schöning²  Jürgen Drewe³  Christoph Helma⁴ 

[1] Berlin Institute for Medical Systems Biology, Max Delbrück Center for Molecular Medicine in the Helmholtz Association, Berlin, Germany;Clinical Pharmacology and Toxicology, Department of General Internal Medicine, University Hospital Bern, University of Bern, Inselspital, Bern, Switzerland;Department of Clinical Pharmacology, University Hospital Basel, University of Basel, Basel, Switzerland;In Silico Toxicology Gmbh, Basel, Switzerland;Max Zeller Söhne AG, Romanshorn, Switzerland;

关键词: mutagenicity;    lazar;    openbabel;    CDK;    machine learning;    tensorflow;   

DOI  :  10.3389/fphar.2021.708050

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown