Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

QSAR Models for Active Substances Against Pseudomonas aeruginosa Using Disk-diffusion Test Data

Version 1 : Received: 4 February 2021 / Approved: 4 February 2021 / Online: 4 February 2021 (22:04:37 CET)

How to cite: Bugeac, C.A.; Ancuceanu, R.; Dinu, M. QSAR Models for Active Substances Against Pseudomonas aeruginosa Using Disk-diffusion Test Data. Preprints 2021, 2021020147 (doi: 10.20944/preprints202102.0147.v1). Bugeac, C.A.; Ancuceanu, R.; Dinu, M. QSAR Models for Active Substances Against Pseudomonas aeruginosa Using Disk-diffusion Test Data. Preprints 2021, 2021020147 (doi: 10.20944/preprints202102.0147.v1).

Abstract

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 Naive Bayes Classifier). The main descriptors used in building the models belonged to the families of adjacency matrix, constitutional descriptors, first highest eigenvalue of Burden matrix, centered Moreau-Broto autocorrelation, and averaged and centered Moreau-Broto autocorrelation descriptors. A total of 32 models were built, of which 28 were selected and stacked to create a meta-model. In terms of balanced accuracy, the best performance was provided by KNN, SVM and AdaBoost algorithms, but the ensemble method had slightly superior results in nested cross-validation.

Subject Areas

Pseudomonas; antimicrobial; QSAR; chemical descriptors; machine-learning; KNN; support vector classifier; AdaBoost

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