Evolution of Machine Learning Algorithms in the Prediction and Design of Anticancer Peptides

Abstract

Peptides act as promising anticancer agents due to their ease of synthesis and modifications, enhanced tumor penetration, and less systemic toxicity. However, only limited success has been achieved so far, as experimental design and synthesis of anticancer peptides (ACPs) are prohibitively costly and time-consuming. Furthermore, the sequential increase in the protein sequence data via highthroughput sequencing makes it difficult to identify ACPs only through experimentation, which often involves months or years of speculation and failure. All these limitations could be overcome by applying machine learning (ML) approaches, which is a field of artificial intelligence that automates analytical model building for rapid and accurate outcome predictions. Recently, ML approaches hold great promise in the rapid discovery of ACPs, which could be witnessed by the growing number of MLbased anticancer prediction tools. In this review, we aim to provide a comprehensive view on the existing ML approaches for ACP predictions. Initially, we will briefly discuss the currently available ACP databases. This is followed by the main text, where state-of-the-art ML approaches working principles and their performances based on the ML algorithms are reviewed. Lastly, we discuss the limitations and future directions of the ML methods in the prediction of ACPs.