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 and Chemistry Development Kit (CDK) descriptors. Crossvalidation acc...

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Autores principales: Helma, Christoph, Schöning, Verena, Drewe, Jürgen, Boss, Philipp
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2021
Materias:
Pharmacology
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8339974/
https://www.ncbi.nlm.nih.gov/pubmed/34366864
http://dx.doi.org/10.3389/fphar.2021.708050
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author Helma, Christoph
Schöning, Verena
Drewe, Jürgen
Boss, Philipp
author_facet Helma, Christoph
Schöning, Verena
Drewe, Jürgen
Boss, Philipp
author_sort Helma, Christoph
collection PubMed
description 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.
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spelling pubmed-83399742021-08-06 A Comparison of Nine Machine Learning Mutagenicity Models and Their Application for Predicting Pyrrolizidine Alkaloids Helma, Christoph Schöning, Verena Drewe, Jürgen Boss, Philipp Front Pharmacol Pharmacology 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. Frontiers Media S.A. 2021-07-22 /pmc/articles/PMC8339974/ /pubmed/34366864 http://dx.doi.org/10.3389/fphar.2021.708050 Text en Copyright © 2021 Helma, Schöning, Drewe and Boss. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Pharmacology
Helma, Christoph
Schöning, Verena
Drewe, Jürgen
Boss, Philipp
A Comparison of Nine Machine Learning Mutagenicity Models and Their Application for Predicting Pyrrolizidine Alkaloids
title A Comparison of Nine Machine Learning Mutagenicity Models and Their Application for Predicting Pyrrolizidine Alkaloids
title_full A Comparison of Nine Machine Learning Mutagenicity Models and Their Application for Predicting Pyrrolizidine Alkaloids
title_fullStr A Comparison of Nine Machine Learning Mutagenicity Models and Their Application for Predicting Pyrrolizidine Alkaloids
title_full_unstemmed A Comparison of Nine Machine Learning Mutagenicity Models and Their Application for Predicting Pyrrolizidine Alkaloids
title_short A Comparison of Nine Machine Learning Mutagenicity Models and Their Application for Predicting Pyrrolizidine Alkaloids
title_sort comparison of nine machine learning mutagenicity models and their application for predicting pyrrolizidine alkaloids
topic Pharmacology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8339974/
https://www.ncbi.nlm.nih.gov/pubmed/34366864
http://dx.doi.org/10.3389/fphar.2021.708050
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