## 4 Discussion
To this end, we have computed an extended set of features including amino acid composition, dipeptide composition, pseudo amino acid composition, CTD of physico-chemical properties, different autocorrelations, quasi-sequence-order descriptors, and sequence order coupling number, separately for Gram-positive and Gram-negative datasets. We have compared the performances of the models which use only the physico-chemical features with the models which use an extended set of features, separately for Gram-positive and Gram-negative datasets. As shown in Tables 7 and 8, the addition of an extended set of features did not improve performance metrics, and even lowered the metrics slightly. For the Gram-positive dataset, when we applied feature selection on the extended set of features, we observed that all three selected features (isoelectric point, net charge, disordered conformation propensity) belong to the physico-chemical features category. Among 1507 different descriptors belonging to the structure-based, linguistic-based, sequence-based, and physico-chemical-based classes in the extended dataset, the identification of the three physico-chemical descriptors as the top three scoring features was noteworthy. These three physico-chemical descriptors are computed from sequence information only. A similar observation is reported for miRNAs in [127, 128, 129]. In these studies, it is shown for miRNAs that the use of sequence information only (k-mer representation) is just enough for the prediction, while different studies use structure information, motif representation, and k-mer for that purpose. [PERSON] et al. [61] imported AMPs with reported quantitative hemolytic activity from DBAASP and extracted 1541 features from physico-chemical, structure, and sequence categories. They trained models using SVM classifier with radial basis function (RBF) and Polynomial kernels, Linear Support Vector Classifier (LSVC), RF, Naive Bayes and kNN. In their experiments, the top three scoring features (aggregation propensity, polarity, charge density) among the 1541 features belong to the physico-chemical cate gory. They have also applied feature selection and reported the performance metrics for 90 selected features among 1541 features. Among the selected 90 features, three features (aggregation propensity in vivo, charge density, isoelectric point) in the top ten scoring features belong to the physico-chemical features. In their study, the performance metrics reported after feature selection (including 90 features) were very close to the performance metrics before applying feature selection (with 1541 features). The models developed in this study were mainly based on physico-chemical features because as a continuation of this work, we are working on de novo antimicrobial peptide design by using the datasets that we have compiled in this study, and by using the classification models that we have developed in this study, separately for Gram-positive and Gram-negative datasets. Before synthesizing de novo peptides, we would like to computationally evaluate the antimicrobial activity of these candidate peptides using our classification model. During the wet-lab part of our future studies (when we synthesize those peptides), we need to know about those physico-chemical features. As a future work, once we identify a promising candidate (a de novo peptide), we plan to continue with the recombinant peptide production steps in wet-lab, and we plan to test the antimicrobial activity of this peptide against Gram-positive or Gram-negative bacteria in wet-lab.