## 4 Discussion  
Antimicrobial peptides are characterized as positively charged, short-chain compounds which act against a wide range of microorganisms by interacting with the target cell components using different mechanisms [53]. The fact that AMPs have various mechanisms of action on the membrane makes bacterial resistance formation against them more complex compared to the conventional therapeutics. Therefore, AMPs are an attractive alternative to combat resistant bacteria [9]. However, AMPs derived from natural sources have some disadvantages such as low stability, salt tolerance, and high toxicity that limit their therapeutic applications. Computational studies on AMPs help us to better understand the effect of the physico-chemical properties of the peptides on stability and activity of AMPs. With the help of computational approaches in the study of AMPs, now it has become possible to overcome the above-mentioned difficulties and to design peptides with broad-spectrum activities and good stability [5]. In this study, a machine learning-based approach was developed for the first time to separately predict the peptides active against Gram-positive and Gram-negative bacteria. It is well known that Gram-positive and Gram-negative bacteria have different cell-surface architectures. For example, Gram-negative bacteria have a thin peptidoglycan cell wall, surrounded by an outer membrane mainly containing lipopolysaccharideacharide. Gram-positive bacteria lack an outer membrane but the cell wall contains a thicker peptidoglycan layer and teichoic acids. Cell surface envelopes play a crucial role in the penetration and initial interaction of AMP. Therefore, the prediction of the antimicrobial activity of AMPs needs to be considered separately for these two different bacterial groups. For this purpose, in this study, two different data sets were created by selecting peptides that are active against (i) _E. coli_, _P. aeruginosa_, and _A. baumannii_ species for Gram-negative bacteria and (ii) _S. aureus_, _L. monocytogenes_, and _B. cereus_ species for Gram-positive bacteria. As mentioned above, in this study, we have an important biological question. The whole study aims to answer this biological question via developing a specific classification model for AMP prediction, separately for Gram-positive and Gram-negative datasets. For this reason, we created a new AMP prediction dataset from publicly available DBAASP dataset by filtering for specific values (as shown in Figure 1 and as explained in detail in the Section 2.1). In this study, we have only focused on linear cationic antimicrobial peptides. Among these peptides, we selected the peptides having antimicrobial activity against above-mentioned species. For each peptide, in order to define the activity against a group of bacteria (positive class label), we have utilized MIC values. Since we focus on the membrane-targeting AMPs, we have selected the peptides with lengths ranging from 20 to 50 aa. Here we focused on non-hemolytic peptides because in therapeutic applications, the prediction of non-hemolytic peptides is reported as more important than the hemolytic peptides for the elimination of the detrimental effects of AMPs on the host. Since there are many peptides with very similar sequences, we eliminated those with a similarity rate of 80% or more using the CD-HIT program [52]. We carried out our classification procedure with the remaining peptides. The antimicrobial activity of the peptides (AMP or non-AMP class) was predicted separately for each bacterial group by using different physico-chemical properties. For each bacterial group, different models were developed using different classification algorithms. We have experimented with several machine learning methods including Adaboost, Logitboost, Decision Tree, RF, SVM, and stacking classifiers using 100-fold MCCV. In our experiments using ten physico-chemical features, we have observed that RF outperforms other classifiers.