Christoph Helma1, Verena Schöning2, Jürgen Drewe3,4, Philipp Boss5. 1. In Silico Toxicology Gmbh, Basel, Switzerland. 2. Clinical Pharmacology and Toxicology, Department of General Internal Medicine, University Hospital Bern, University of Bern, Inselspital, Bern, Switzerland. 3. Max Zeller Söhne AG, Romanshorn, Switzerland. 4. Department of Clinical Pharmacology, University Hospital Basel, University of Basel, Basel, Switzerland. 5. Berlin Institute for Medical Systems Biology, Max Delbrück Center for Molecular Medicine in the Helmholtz Association, Berlin, Germany.
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.
Random forest, support vector machine, logistic regression, neural networks and k-nearest neighbor (n class="Chemical">lazar) algorithms, were applied to a new n>n class="Species">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.
Authors: Jürgen Drewe; Ernst Küsters; Felix Hammann; Matthias Kreuter; Philipp Boss; Verena Schöning Journal: Molecules Date: 2021-10-28 Impact factor: 4.411