AI-Driven Design of Cell-Penetrating Peptides for Therapeutic Biotechnology

BackgroundCell-penetrating peptides (CPPs) are short sequences of amino acids, typically ranging from 5 to 30 residues, known for their ability to penetrate cell membranes and facilitate the transport of macromolecules into cells. This characteristic positions CPPs as powerful vectors in various fields, including gene therapy, pharmaceuticals, and clinical research.ObjectiveThis review aims to elucidate the critical role of artificial intelligence (AI) in the discovery, design, and optimization of CPPs, emphasizing how these technologies enhance their therapeutic applications.MethodsThe review systematically analyzes recent developments in AI-assisted methodologies, including machine learning algorithms that have been employed for the identification, prediction, and validation of CPPs. Various AI tools and their contributions to the CPP research landscape are explored.ResultsThe integration of AI and machine learning methodologies has accelerated the process of CPP discovery and optimization. These technologies allow for the rapid processing of extensive biological data, enabling researchers to identify potential CPP candidates more efficiently and predict their effectiveness in cellular uptake. The review highlights significant advancements in high-throughput screening, predictive modeling, and validation techniques driven by AI.ConclusionsThe application of AI in the domain of CPPs has transformed traditional approaches to peptide design, significantly enhancing their therapeutic potential. The findings suggest that continued investment in AIpowered tools and methodologies will further revolutionize the field, fostering the development of novel CPPs that can improve delivery mechanisms for therapeutics and contribute to advancements in gene therapy and other biomedical applications.