Aims: This article presents models of artificial neural networks (ANN) 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 microbial growth E. 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 molecular descriptors. The transformation of chemical information into a useful number is a main result of this operation. The designed regression and classification ANN 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 modelling technique, which allows not only to optimize and minimize labour costs but also to increase food safety.
Keywords: Escherichia coli; artificial neural network; food safety; imidazoles; predicted antimicrobial activity.
© 2020 The Society for Applied Microbiology.