A comparison of nine machine learning mutagenicity models and their application for predicting pyrrolizidine alkaloids

Tools

Preview

Item Type:	Article
Title:	A comparison of nine machine learning mutagenicity models and their application for predicting pyrrolizidine alkaloids
Creators Name:	Helma, C., Schöning, V., Drewe, J. and Boss, P.
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.
Keywords:	Mutagenicity, Lazar, Openbabel, CDK, Machine Learning, Tensorflow, Pyrrolizidine Alkaloids
Source:	Frontiers in Pharmacology
ISSN:	1663-9812
Publisher:	Frontiers Media SA
Volume:	12
Page Range:	708050
Date:	22 July 2021
Official Publication:	https://doi.org/10.3389/fphar.2021.708050
PubMed:	View item in PubMed

Repository Staff Only: item control page

Download Statistics

Downloads

Downloads per month over past year