Integration of structure-activity relationship and artificial intelligence systems to improve in silico prediction of ames test mutagenicity

The Ames mutagenicity test in Salmonella typhimurium is a bacterial short-term in vitro assay aimed at detecting the mutagenicity caused by chemicals. Mutagenicity is considered as an early alert for carcinogenicity. After a number of decades, several (Q)SAR studies on this endpoint yielded enough evidence to make feasible the construction of reliable computational models for prediction of mutagenicity from the molecular structure of chemicals. In this study, we propose a combination of a fragment-based SAR model and an inductive database. The hybrid system was developed using a collection of 4337 chemicals (2401 mutagens and 1936 nonmutagens) and tested using 753 independent compounds (437 mutagens and 316 nonmutagens). The overall error of this system on the external test set compounds is 15% (sensitivity = 15%, specificity = 15%), which is quantitatively similar to the experimental error of Ames test data (average interlaboratory reproducibility determined by the National Toxicology Program). Moreover, each single prediction is provided with a specific confidence level. The results obtained give confidence that this system can be applied to support early and rapid evaluation of the level of mutagenicity concern.