Bridging machine learning and peptide design for cancer treatment: a comprehensive review

Abstract

Anticancer peptides (ACPs) offer a promising alternative to traditional cancer therapies due to their specificity and reduced side effects. The development of ACPs using machine learning (ML) and deep learning (DL) follows a structured process, beginning with sequence collection from in vitro and in vivo experiments. Key features such as hydrophobicity and secondary structure are extracted, and classification models categorize peptides based on their properties. ML models predict anticancer effectiveness, followed by toxicity checks and Structure-Activity Relationship (SAR) analysis to ensure safety and efficacy, with validation tests confirming their activity. This review explores how the automated design of ACPs can be enhanced by leveraging advanced ML and DL techniques. These methods, with their ability to automate feature selection and activity prediction, have significantly improved the efficiency and accuracy of peptide discovery. This structured approach holds high potential to guide researchers in the automated design of ACPs, accelerating the discovery of effective peptides while ensuring safety. Special attention is given to new approaches such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Generative Adversarial Networks (GANs), which show promise in addressing key challenges like data imbalance and computational complexity. Moreover, we examine the latest published research to compare the performance of various ML models in ACP prediction. By considering these advancements and challenges, this review outlines future opportunities for improving the scalability and reliability of ACP discovery using AI-driven techniques. This structured approach underscores the transformative impact of automation in peptide design, pushing the boundaries of modern cancer therapy development.