Helma, Christoph; Schöning, Verena; Drewe, Jürgen; Boss, Philipp (2021). A Comparison of Nine Machine Learning Mutagenicity Models and Their Application for Predicting Pyrrolizidine Alkaloids. Frontiers in Pharmacology, 12, p. 708050. Frontiers 10.3389/fphar.2021.708050
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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.
Item Type: |
Journal Article (Original Article) |
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Division/Institute: |
04 Faculty of Medicine > Department of General Internal Medicine (DAIM) > Clinic of General Internal Medicine |
UniBE Contributor: |
Schöning, Verena |
Subjects: |
600 Technology > 610 Medicine & health |
ISSN: |
1663-9812 |
Publisher: |
Frontiers |
Language: |
English |
Submitter: |
Tobias Tritschler |
Date Deposited: |
13 Jan 2022 11:55 |
Last Modified: |
05 Dec 2022 16:01 |
Publisher DOI: |
10.3389/fphar.2021.708050 |
PubMed ID: |
34366864 |
BORIS DOI: |
10.48350/163773 |
URI: |
https://boris.unibe.ch/id/eprint/163773 |