Title:Evolution of Machine Learning Algorithms in the Prediction and Design of Anticancer Peptides
VOLUME: 21 ISSUE: 12
Author(s):Shaherin Basith , Balachandran Manavalan, Tae Hwan Shin, Da Yeon Lee and Gwang Lee*
Affiliation:Department of Physiology, Ajou University School of Medicine, Suwon, Department of Physiology, Ajou University School of Medicine, Suwon, Department of Physiology, Ajou University School of Medicine, Suwon, Department of Physiology, Ajou University School of Medicine, Suwon, Department of Physiology, Ajou University School of Medicine, Suwon
Keywords:Cancer, anticancer peptides, machine learning, support vector machine, random forest, ACPs.
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.
