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Application of artificial neural networks to prediction of new substances with antimicrobial activity against Escherichia coli
Author(s) -
Badura A.,
Krysiński J.,
Nowaczyk A.,
Buciński A.
Publication year - 2021
Publication title -
journal of applied microbiology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.889
H-Index - 156
eISSN - 1365-2672
pISSN - 1364-5072
DOI - 10.1111/jam.14763
Subject(s) - artificial neural network , antimicrobial , biological system , artificial intelligence , quantitative structure–activity relationship , regression , escherichia coli , machine learning , set (abstract data type) , linear regression , biochemical engineering , data set , imidazole , computer science , mathematics , chemistry , biology , engineering , biochemistry , statistics , organic chemistry , gene , programming language
Abstract 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.