In silico screening of chemicals for bacterial mutagenicity using electrotopological E-state indices and MDL QSAR software

Abstract

Quantitative structure–activity relationship (QSAR) software offers a rapid, cost effective means of prioritizing the mutagenic potential of chemicals. MDL QSAR models were developed using atom-type E-state indices and non-parametric discriminant analysis. Models were developed for Salmonella typhimurium gene mutation, combining results from strains TA97, TA98, TA100, TA1535, TA1536, TA1537, and TA1538 ( n = 3228), and Escherichia coli gene mutation tests WP2, WP100, and polA ( n = 472). Composite microbial mutation models ( n = 3338) were developed combining all Salmonella, E. coli, and the Bacillus subtilis rec spot test study results. The datasets contained 74% non-pharmaceuticals and 26% pharmaceuticals. Salmonella and microbial mutagenesis external validation studies included a total of 1444 and 1485 compounds, respectively. The average specificity, sensitivity, positive predictivity, concordance, and coverage of Salmonella models was 76, 81, 73, 78, and 98%, respectively, with similar performance for the microbial mutagenesis models. MDL QSAR and discriminant analysis provides rapid and highly automated mutagenicity screening software with good specificity, sensitivity, and coverage that is simpler and requires less user intervention than other similar software. MDL QSAR modules for microbial mutagenicity can provide efficient and cost effective large scale screening of compounds for mutagenic potential for the chemical and pharmaceutical industry.