ABSTRACT
The computational prediction of genotoxicity is very important for the early detection and classification of the chemical entities that can cause carcinogenicity in humans. This chapter confers key scientific advancements and improvements in the prediction of Ames mutagenicity and in-vitro chromosomal damage.
The performance and constraints of computational approaches have been discussed for published and internal validation exercises. The application to the modern drug discovery paradigm is also discussed in this chapter.
This chapter highlights recent scientific literature for the prediction of Ames mutagenicity and chromosomal damage, and appreciation of the factors has been discussed which limits the predictive performance of in-silico systems. Thus, it can be concluded that the deficiency of mechanistic structure–activity relationships and partial access to high-quality proprietary data are holding computational genetic toxicity from reaching higher predictive levels.
