Antimicrobial Activity Classification of Imidazolium Derivatives Predicted by Artificial Neural Networks.

Journal: Pharmaceutical research
PMID: 38632156

Abstract

PURPOSE: This study assesses the Multilayer Perceptron (MLP) neural network, complemented by other Machine Learning techniques (CART, PCA), in predicting the antimicrobial activity of 140 newly designed imidazolium chlorides against Klebsiella pneumoniae before synthesis. Emphasis is on leveraging molecular properties for predictive analysis.

Authors

  • Andżelika Lorenc
    Department of Biopharmacy, Faculty of Pharmacy, Collegium Medicum in Bydgoszcz, Nicolaus Copernicus University in Toruń, dr A. Jurasza 2, 85-089, Bydgoszcz, Poland. andzelika.lorenc@cm.umk.pl.
  • Anna Badura
    Department of Biopharmacy, the Ludwik Rydygier Collegium Medicum in Bydgoszcz, the Nicolaus Copernicus University in Toruń, Toruń, Poland.
  • Maciej Karolak
    Department of Pharmaceutical Technology, Faculty of Pharmacy, Collegium Medicum in Bydgoszcz, Nicolaus Copernicus University in Toruń, dr A. Jurasza 2, 85-089, Bydgoszcz, Poland.
  • Łukasz Pałkowski
    Department of Pharmaceutical Technology, Faculty of Pharmacy, Collegium Medicum in Bydgoszcz, Nicolaus Copernicus University in Toruń, dr A. Jurasza 2, 85-089, Bydgoszcz, Poland.
  • Łukasz Kubik
    Department of Biopharmaceutics and Pharmacodynamics, Medical University of Gdańsk, Gen. J. Hallera 107, 80-416, Gdańsk, Poland.
  • Adam Buciński
    Department of Biopharmacy, the Ludwik Rydygier Collegium Medicum in Bydgoszcz, the Nicolaus Copernicus University in Toruń, Toruń, Poland.

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