# Explainable Machine Learning Model to Accurately Predict Protein-Binding Peptides
## 1 Introduction  
Enzymes are crucial for the biological functions of living organisms. They serve as catalysts to catalyze and regulate biochemical reaction pathways [1; 2]. Searching ligands to modulate enzyme functions is one of the central topics for disease diagnosis and therapeutics [3; 4; 5]. Recent studies suggest that peptides are promising molecules for modulating enzyme function due to their advantages in large chemical diversity, biocompatibility, and well-established methods for library synthesis [6; 7; 8; 9]. For example, peptides and their derivatives can be used to inhibit or activate enzyme catalysis, mediate cell receptor response, and facilitate transmembrane delivery [7; 10]. Traditional ligands and drug discovery rely on the synthesis of large chemical libraries. High-throughput screening or selection is often used to identify ligands for binding to a target protein, such as in vitro mRNA display [8], phage display [11], bead-based library screening [12], protein direct evolution [13], aptamer selection [14], peptide microarray [7; 10; 15], and fragment-based design of small molecules [16]. Unfortunately, these approaches are all very time-consuming and costly. The recent progress of machine learning (ML) and artificial intelligence (AI) have advanced the chemical discovery process [17]. For example, by using a dataset of existing molecular libraries with known functions, ML can be trained to analyze large datasets of ligand structures, sequences, and binding affinities and to predict potential interactions more accurately [18]. During the past two decades, a wide range of ML models has been proposed to predict different types of protein-binding peptides [19; 20; 21; 22; 23]. These studies can generally be categorized into two groups. First are studies that introduced new features to extract more discriminatory information for the prediction task [24; 25; 26]. These studies mainly extract features based on sequential, structural, physicochemical, and evolutionary-based information [27; 28]. For instance, [PERSON] and [PERSON] used chemical interactions and physicochemical properties of amino acids for feature extraction [29]. Later on, [PERSON] et al. extended the use of physicochemical properties and also included sequential-based features to predict protein-binding peptides [24]. In a different study, [PERSON] et al., employed structural features to detect therapeutic peptides [27]. The structural features are mostly extracted based on the predicted values of the protein local or secondary structure of the proteins using tools such as PSIPRED or SPIDER [30; 31; 32]. Other studies, such as [PERSON] et al., used evolutionary information to add to former types of features for better performance [33]. Recent studies have shifted their focus to using Deep Learning (DL) models to automatically extract features from protein sequences instead of different types of properties [34; 35; 36; 33]. However, extracting effective features is still considered a common and effective method to tackle this problem [37]. The second group of studies aims to use effective ML and DL models to predict protein-binding peptides. Among traditional machine learning models, Support Vector Machine (SVM), Random Forest (RF), and Adaptive Boosting have been shown to be very effective for this task [37; 38; 39; 40; 41; 42; 26]. As mentioned earlier, recent studies have shifted their focus to using DL to tackle this problem. However, for smaller datasets, complex DL models cannot be trained properly. Instead, traditional ML methods demonstrate better performance for these cases. In addition, studying the explainability of the complex DL models is more challenging. Therefore, there is a demand for an accurate and explainable model to tackle this problem. Another challenge is to have reliable and large enough datasets to use as a benchmark to train machine learning models to predict common protein-binding peptides. Most of the currently used datasets are either very small or have low resolution.