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

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 against it of highest priority. Whereas minimum inhibitory concentration (MIC) values have been previously for QSAR model development, disk diffusion results (inhibition zones) not apparently this purpose in literature we decided explore their use sense. We developed multiple methods using several machine learning algorithms (support vector classifier, K nearest neighbors, random forest decision tree AdaBoost logistic regression naïve Bayes classifier). four sets molecular descriptors fingerprints three different data balancing, together “native” set. In total, 32 models were built each set or fingerprint balancing method, which 28 selected stacked create meta-models. terms balanced accuracy, best performance was provided by KNN, but ensemble method had slightly superior nested cross-validation.