## 1 Introduction
They used AMP and non-AMP data with different ratios, where 19 different ratios were used in total; 1:3 ratio yielded 96% accuracy with 10-fold cross-validation technique and reduced feature sets [23]. [PERSON] et al. combined sequence alignment with feature selection methods for classification of AMPs [29]. [PERSON] et al. modeled a two-level classifier. First level is for classifying peptide sequences as an AMP, and the second level is to separate these AMPs into 10 functional categories [21]. There are many computational tools to predict AMPs based on machine learning approaches [17, 30, 31, 32, 33, 34]. Additionally, deep learning methods have been used to apply to antimicrobial peptides prediction problems. [PERSON] et al. presented a method called deepAMP for sequences shorter than 30 aa. In their method, they used an optimal feature set of reduced amino acid composition with convolutional neural network and obtained 77% accuracy. They also compared their results with RF and SVM algorithms. While the RF model gives close accuracy (75%) to CNN, the model used for SVM has a lower accuracy (72%) [35]. [PERSON] et al. designed a deep neural network which consists of an embedding layer and multi-convolutional layers for AMP identification. Compared with the existing models, their model achieved a higher accuracy score (92%) [36]. [PERSON] et al. used self-organizing maps as input layers for their feedforward neural network on AMP data and obtained 92% reclassification accuracy with balanced prediction on sam ples [37]. [PERSON] et al. reported a convolutional neural network model for the classification and regression of AMPs [38]. They used Minimum Inhibition Concentration (MIC) values for regression and compared with ridge regression and kNN algorithms. They showed that CNN has better root mean squared error value (0.501) than others. Moreover, for the classification part, when their CNN model is compared with other state-of-the-art methods, they have shown that higher prediction performance (97%) is obtained. [PERSON] et al. proposed a new feature selection approach to concentrate on molecular descriptors [39]. Their approach is applied on six benchmark datasets for evaluation. Additionally, they compared their results with state-of-the-art prediction tools and showed that their model outperforms these tools for prediction of antimicrobial and antibacterial peptides. In addition to the above-mentioned research efforts, some recent studies also used deep neural networks for the prediction of antimicrobial peptides [40; 41; 42; 43]. However, there is no standardization in terms of the use of machine learning methods for the AMP prediction. Nowadays, antimicrobial peptide databases provide comprehensive information on thousands of natural or synthetic antimicrobial peptides. The peptide sequences deposited in these databases can be utilized for de novo design of AMPs using computer-aided approaches [44; 45]. However, in these databases, there is no standardization in terms of the experimental methods that are used to measure the activity of the AMPs in vitro. On the other hand, the antimicrobial activities of several AMPs have been predicted in silico. However, these algorithms do not take into account the physico-chemical and structural properties of the peptides and the mechanism of antimicrobial action against specific target microorganisms. Therefore, there is a need for new approaches based on the structure-activity relationship to accurately predict the antimicrobial activity of candidate peptides before synthesis. In the last decade, a vast number of studies focused on the development of computational methods for determining the antimicrobial activity of natural or synthetic AMPs. However, the vast majority of these methods do not take into account the specific properties of bacterial targets. However, an AMP can exhibit different mechanisms of action against different target microorganisms.