Hierarchical machine learning model predicts antimicrobial peptide activity against .

is a dangerous pathogen which causes a vast selection of infections. Antimicrobial peptides have been demonstrated as a new hope for developing antibiotic agents against multi-drug-resistant bacteria such as . Yet, most studies on developing classification tools for antimicrobial peptide activities do not focus on any specific species, and therefore, their applications are limited. Here, by using an up-to-date dataset, we have developed a hierarchical machine learning model for classifying peptides with antimicrobial activity against . The first-level model classifies peptides into AMPs and non-AMPs. The second-level model classifies AMPs into those active against and those not active against this species. Results from both classifiers demonstrate the effectiveness of the hierarchical approach. A comprehensive set of physicochemical and linguistic-based features has been used, and after feature selection steps, only some physicochemical properties were selected. The final model showed the F1-score of 0.80, recall of 0.86, balanced accuracy of 0.80, and specificity of 0.73 on the test set. The susceptibility to a single AMP is highly varied among different target species. Therefore, it cannot be concluded that AMP candidates suggested by AMP/non-AMP classifiers are able to show suitable activity against a specific species. Here, we addressed this issue by creating a hierarchical machine learning model which can be used in practical applications for extracting potential antimicrobial peptides against from peptide libraries.