Application of artificial neural networks to prediction of new substances with antimicrobial activity against Escherichia coli.

AIMS This article presents models of artificial neural networks employed to predict the biological activity of chemical compounds based of their structure. Regression and classification models were designed to determine antimicrobial properties of quaternary ammonium salts against Escherichia coli strain. METHODS AND RESULTS The minimum inhibitory concentration (MIC) microbial growth Escherichia coli was experimentally determined by the serial dilution method for a series of 140 imidazole derivatives. Then, three-dimensional models for imidazole chlorides were constructed with computational chemistry methods which allowed to calculate of molecules molecular descriptors. The transformation of chemical information into a useful number number is a main result of this operation. The designed regression and classification artificial neural network models were characterized by a high predictive ability (Classification accuracy was 95%, Regression model: learning set R=0.87, testing set R=0.91, validation set R=0.89). CONCLUSIONS Artificial neural networks can be successfully used to find potential antimicrobial preparations SIGNIFICANCE AND IMPACT OF THE STUDY: The neural networks are a very elaborate modeling technique, which allows not only to optimize and minimize labor costs, but also to increase food safety.