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

Journal: Journal of applied microbiology

PMID: 32619323

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.

Authors

A Badura

Department of Biopharmacy, Faculty of Pharmacy, Collegium Medicum in Bydgoszcz, Nicolaus Copernicus University in Toruń, Bydgoszcz, Poland.
J Krysiński

Department of Pharmaceutical Technology, Faculty of Pharmacy, Collegium Medicum in Bydgoszcz, Nicolaus Copernicus University in Toruń, Bydgoszcz, Poland.
A Nowaczyk

Department of Organic Chemistry, Faculty of Pharmacy, Collegium Medicum in Bydgoszcz, Nicolaus Copernicus University in Toruń, Bydgoszcz, Poland.
A Buciński

Department of Biopharmacy, Faculty of Pharmacy, Collegium Medicum in Bydgoszcz, Nicolaus Copernicus University in Toruń, Bydgoszcz, Poland.

Keywords

Anti-Bacterial Agents Drug Discovery Escherichia coli Imidazoles Microbial Sensitivity Tests Neural Networks, Computer Quantitative Structure-Activity Relationship Quaternary Ammonium Compounds

External Resources

View on PubMed Access via DOI PubMed (32619323)

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

Abstract

Authors

Keywords

External Resources

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