Three new consensus QSAR models for the prediction of Ames genotoxicity

Mutagenesis
Joseph R VotanoWeida Tong

Abstract

Three QSAR methods, artificial neural net (ANN), k-nearest neighbors (kNN), and Decision Forest (DF), were applied to 3363 diverse compounds tested for their Ames genotoxicity. The ratio of mutagens to non-mutagens was 60/40 for this dataset. This group of compounds includes >300 therapeutic drugs. All models were developed using the same initial set of 148 topological indices: molecular connectivity chi indices and electrotopological state indices (atom-type, bond-type and group-type E-state), as well as binary indicators. While previous studies have found logP to be a determining factor in genotoxicity, it was not found to be important by any modeling method employed in this study. The three models yielded an average training/test concordance value of 88%, with a low percentage of false positives and false negatives. External validation testing on 400 compounds not used for QSAR model development gave an average concordance of 82%. This value increased to 92% upon removal of less reliable outcomes, as determined by a reliability criterion used within each model. The ANN model showed the best performance in predicting drug compounds, yielding 97% concordance (34/35 drugs) after the removal of less reliable predictions. The app...Continue Reading

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