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

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 naïve Bayes classifier). We used four sets

SUBMITTER: Bugeac C 

PROVIDER: S-EPMC8003670 | biostudies-literature | 2021 Mar

REPOSITORIES: biostudies-literature