# ABPCaps: A Novel Capsule Network-Based Method for the Prediction of Antibacterial Peptides
## 1 Introduction  
Antibiotic resistance has become one of the most urgent health concerns worldwide. Despite significant progress in antibiotic research since the discovery of the first antibiotic in 1928, the emergence of drug-resistant bacteria continues to be a challenge for infectious disease researchers [1]. The alarming increase in antibiotic resistance has led to the rise of superbugs, such as methicillin-resistant _Staphylococcus aureus_ (MRSA), which poses a severe threat in both hospital and community settings [2]. Even the most commonly used antibiotics, such as amoxicillin and eturoxime, have demonstrated high rates of resistance in strains of _Escherichia coli_[2]. The misuse of antibiotics has contributed significantly to the rise of drug-resistant bacteria, highlighting the urgent need for alternative treatments. Antimicrobial peptides (AMPs) have emerged as promising alternatives to antibiotics, potentially solving the antibiotic resistance problem [3; 4]. Among them, antibacterial peptides (ABPs) have been the focus of considerable attention due to their ability to inhibit various types of bacterial pathogens and treat infections. Their broad-spectrum activity and low propensity to induce resistance make them an attractive alternative to conventional antibiotics. With the growing challenge of antibiotic resistance, the potential of AMPs and ABPs to develop new treatments and therapies cannot be overstated. Over the past few years, the development and identification of AMPs has received considerable attention from researchers. Several databases and identification tools have been developed to facilitate this research. ANTIMIC, which integrates a number of tools and contains 1700 peptides, is one of the earliest and most prominent AMP databases [5]. Another prominent AMP database is LAMP2, which contains over 23,000 peptide sequences classified according to their source: experimental, predicted or patent [6]. The latest version of DRAMP, on the other hand, contains over 22,000 entries, including 181 stapled AMPs [7]. Finally, the latest version of dbAMP contains over 26,000 AMPs and antimicrobial proteins from more than 3000 organisms [8]. Several machine learning-based methods have been developed for identification of AMPs, as assessing the activity of AMPs is crucial for determining their potential as drugs [9; 10; 11; 12; 13; 14; 15; 16]. Table 1 summarizes existing AMP prediction tools, with random forests (RF) and support vector machines (SVM) showing particular success [9]. In addition, [PERSON] et al. developed iAMP-2L, a two-stage multi-label classifier that can predict whether a given peptide is an AMP and identify its functional target [10]. More recently, deep learning-based models have been used to identify AMPs, with the development of the Deep-AmPEP30 method based on a convolutional neural network (CNN) [11]. To build a predictive model for AMP identification, [PERSON] et al. integrated CNN and long short-term memory (LSTM) layers [12; 17]. Additionally, a bidirectional LSTM (biLSTM) was used for this task [13]. Although CNNs have proven to be a powerful tool for a wide range of machine learning tasks, they are not without their limitations. One such limitation is the problem of invariance caused by pooling, which can lead to the loss of important spatial relationships between features [18; 19]. In addition, CNNs are often unable to understand the relationships between different features. To address these limitations, researchers have developed a new type of neural network called a capsule network [20]. Capsule networks differ from CNNs in several ways. First, the inputs and outputs of a capsule network are represented in vector form, where each element represents a particular feature of the entity. The length of the vector indicates the probability of that feature being present [21]. Secondly, the use of dynamic routing algorithms between different capsules allows the network to have a better understanding of the relationships between different features.