Maduravani Ramasundaram ¹ Honglae Sohn ^2* Thirumurthy Madhavan ^1*

• ¹ SRM Institute of Science and Technology, Chennai, India
• ² Chosun University, Gwangju, Republic of Korea

Cell-penetrating peptides (CPPs) are highly effective at passing through eukaryotic membranes with a variety of cargo molecules like drugs, proteins, nucleic acids, and nanoparticles without causing significant harm. Creating drug delivery systems with CPP is associated with cancer, genetic disorders, and diabetes, due to their unique chemical properties. Wet lab experiments in drug discovery methods are time-consuming and expensive. Machine learning (ML) techniques can enhance and fasten the drug discovery process with accurate and intricate data quality. ML classifiers like support vector machine (SVM), random forest (RF), gradient boost decision tree (GBDT), and different types of artificial neural networks (ANN) are commonly used for CPP prediction with cross-validation performance evaluation. Functional CPP prediction is improved by these ML strategies by using CPP datasets produced by high-throughput sequencing and computational methods. This review is focused on several ML-based CPP prediction tools. We highlighted the CPP mechanism to understand the basic functioning of CPPs through cells. A comparative analysis of diverse CPP prediction methods was carried out based on their algorithms, dataset size, feature encodings, software utilities, assessment metrics, and prediction scores. The performance of the CPP prediction was evaluated based on accuracy, sensitivity, specificity, and Matthews correlation coefficient (MCC) on independent datasets. In conclusion, this review will encourage the use of ML algorithms for finding effective CPPs, which will impact future research on drug delivery and therapeutics.