## 5 Conclusions  
The main contribution of this paper is the development of two accurate classification models for the prediction of antimicrobial peptides active against (i) Gram-negative and (ii) Gram-positive bacteria, separately. To this end, we have compiled two different datasets for (i) peptides active against Gram-negative bacteria and (ii) peptides active against Gram-positive bacteria, and evaluated different machine learning models for the prediction of antimicrobial peptide activity. In our experiments with 100-fold MCCV, the RF algorithm achieved better results compared to other algorithms for both datasets. At the end of our feature ranking procedure, the net charge was found as the most important feature for Gram-negative dataset and second most important feature for Gram-positive dataset. Moreover, for the Gram-positive dataset, the pl was found as the most important feature, while it was determined as the second most important feature for the Gram-negative dataset. In literature, both net charge and the isoelectric point of a peptide are known to have a considerable effect in terms of determining the activity of AMPs [119]. Hence, our findings are not contradictory with previous results which suggest that net charge and pl are the main factors for strong antimicrobial activity, and this situation further proves the validity of the computational models created in this study. The PCA visualization is applied on the Gram-negative and the Gram-positive dataset, and some outlier samples have been observed. Based on the distribution of the positive and negative labeled samples (peptides having antimicrobial activity vs. non-AMP peptides), certain ranges are defined for each attribute. In our secondary experiments, in which the peptides outside those ranges were eliminated (outlier detection), we observed that the AUC results increased by 7% for both the Gram-negative and Gram-positive dataset. We repeated our experiments using an extended feature set including amino acid composition, pseudo amino acid composition, sequence order, autocorrelation, composition, distribution, and transition of physico-chemical properties. When we run our workflow on these extended feature sets, the performance metrics did not improve, and even lowered slightly. For the Gram-negative dataset, while the extended set of features yielded 98% AUC with LogitBoost, physico-chemical features yielded 99% AUC with RF. For the Gram-positive dataset, while the model using an extended set of features achieved 95% AUC with RF, the generated model using only ten physico-chemical features achieved 97% AUC. When we compared the performance metrics obtained using physico-chemical properties (10 features) with an extended set of features (1507 features), we observed that rather than using a large selection of features, a small number of features yielded better results on both Gram-negative and Gram-positive datasets. Different feature selection methods are applied on the extended dataset for removing redundant features. It is worthwhile to note that for the Gram-positive dataset, among 1507 different descriptors belonging to the structure-based, linguistic-based, sequence-based, and physico-chemical-based classes in the extended dataset, all 3 selected features (isoelectric point, net charge, disordered conformation propensity) are physico-chemical descriptors. After the feature selection is applied on the extended dataset including 1507 features, the AUC values of the models using the top 3 scoring features decreased only by 1% and 2% for the Gram-positive and Gram-negative datasets, respectively. When we compare the performance metrics before and after feature selection is applied, we can deduce that using only 3 features yields satisfactory performance results (96% and 94% AUC) for Gram-negative and Gram-positive datasets, respectively.