Abstract

With their diverse biological activities, peptides are promising candidates for therapeutic applications, showing antimicrobial, antitumor, and hormonal signaling capabilities. De- spite their advantages, therapeutic peptides face challenges such as short half-life, limited oral bioavailability, and susceptibility to plasma degradation. The rise of computational tools and artificial intelligence in peptide research has spurred the development of advanced methodologies and databases that are pivotal in the exploration of these complex macro- molecules. This perspective delves into integrating artificial intelligence in peptide devel- opment, encompassing classifier methods, predictive systems, and the avant-garde design facilitated by deep-generative models like generative adversarial networks and variational autoencoders. There are still challenges, such as the need for processing optimization and careful validation of predictive models. This work outlines traditional strategies for ma- chine learning model construction and training techniques and proposes a comprehensive AI-assisted peptide design and validation pipeline. The evolving landscape of peptide design using artificial intelligence is emphasized, showcasing the practicality of these methods in expediting the development and discovery of novel peptides within the context of peptide- based drug discovery