In silico methods for mutagenicity prediction
| dc.contributor.author | Paskaleva, Vesselina | |
| dc.contributor.author | Cokova, Gergana | |
| dc.date.accessioned | 2025-12-16T09:58:27Z | |
| dc.date.available | 2025-12-16T09:58:27Z | |
| dc.date.issued | 2025-12-04 | |
| dc.description.abstract | This research presents a thorough exploratory data analysis to develop an in silico model for mutagenicity prediction, contributing to Safe-by-Design strategies. Using a publicly available dataset, chemical structures were encoded via a range of molecular fingerprints and descriptors. Multiple machine learning algorithms—including k-nearest neighbors, support vector machines, and random forest—were assessed. Performance was validated through 10- fold cross-validation and further tested on an external dataset. Random Forest emerged as the most effective method, achieving a cross-validation MCC of 0.68. The in-house models showed competitive performance relative to existing publicly available tools. | |
| dc.identifier.issn | 1313-9940 | |
| dc.identifier.uri | https://doi.uni-plovdiv.bg/handle/store/835 | |
| dc.language.iso | en | |
| dc.publisher | Plovdiv University Press "Paisii Hilendarski" | |
| dc.subject | ames mutagenicity | |
| dc.subject | machine learning | |
| dc.subject | QSAR | |
| dc.subject | in silico | |
| dc.title | In silico methods for mutagenicity prediction | |
| dc.type | Article |