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Article

QSAR Models for Active Substances against Pseudomonas aeruginosa Using Disk-Diffusion Test Data

1
Faculty of Pharmacy, Carol Davila University of Medicine and Pharmacy, 6 Traian Vuia Street, Sector 2, 020956 Bucharest, Romania
2
Department of Pharmaceutical Botany and Cell Biology, Faculty of Pharmacy, Carol Davila University of Medicine and Pharmacy, 6 Traian Vuia Street, Sector 2, 020956 Bucharest, Romania
*
Author to whom correspondence should be addressed.
Academic Editor: Alla P. Toropova
Molecules 2021, 26(6), 1734; https://doi.org/10.3390/molecules26061734
Received: 4 February 2021 / Revised: 14 March 2021 / Accepted: 15 March 2021 / Published: 19 March 2021
(This article belongs to the Special Issue QSAR and QSPR: Recent Developments and Applications II)
Pseudomonas aeruginosa is a Gram-negative bacillus included among the six “ESKAPE” microbial species with an outstanding ability to “escape” currently used antibiotics and developing new antibiotics against it is of the highest priority. Whereas minimum inhibitory concentration (MIC) values against Pseudomonas aeruginosa have been used previously for QSAR model development, disk diffusion results (inhibition zones) have not been apparently used for this purpose in the literature and we decided to explore their use in this sense. We developed multiple QSAR methods using several machine learning algorithms (support vector classifier, K nearest neighbors, random forest classifier, decision tree classifier, AdaBoost classifier, logistic regression and naïve Bayes classifier). We used four sets of molecular descriptors and fingerprints and three different methods of data balancing, together with the “native” data set. In total, 32 models were built for each set of descriptors or fingerprint and balancing method, of which 28 were selected and stacked to create meta-models. In terms of balanced accuracy, the best performance was provided by KNN, logistic regression and decision tree classifier, but the ensemble method had slightly superior results in nested cross-validation. View Full-Text
Keywords: pseudomonas; antimicrobial; QSAR; chemical descriptors; machine-learning; KNN; support vector classifier; AdaBoost pseudomonas; antimicrobial; QSAR; chemical descriptors; machine-learning; KNN; support vector classifier; AdaBoost
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MDPI and ACS Style

Bugeac, C.A.; Ancuceanu, R.; Dinu, M. QSAR Models for Active Substances against Pseudomonas aeruginosa Using Disk-Diffusion Test Data. Molecules 2021, 26, 1734. https://doi.org/10.3390/molecules26061734

AMA Style

Bugeac CA, Ancuceanu R, Dinu M. QSAR Models for Active Substances against Pseudomonas aeruginosa Using Disk-Diffusion Test Data. Molecules. 2021; 26(6):1734. https://doi.org/10.3390/molecules26061734

Chicago/Turabian Style

Bugeac, Cosmin A., Robert Ancuceanu, and Mihaela Dinu. 2021. "QSAR Models for Active Substances against Pseudomonas aeruginosa Using Disk-Diffusion Test Data" Molecules 26, no. 6: 1734. https://doi.org/10.3390/molecules26061734

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