![[feed]](/style/images/feed-icon-14x14.png){kind=icon}
|
PDF (Original Article)
- Requires a PDF viewer such as GSview, Xpdf or Adobe Acrobat Reader
4MB |
| Item Type: | Article |
|---|---|
| Title: | A comparison of nine machine learning mutagenicity models and their application for predicting pyrrolizidine alkaloids |
| Creators Name: | Helma, C. and Schöning, V. and 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
Download Statistics
