Unveiling anticancer peptides; from the mechanisms of action to their development through artificial intelligence.

Cancer is a leading cause of death worldwide and a major burden on the healthcare system. Current treatment methods are limited as they have low selectivity, unspecific targeting and increasing multidrug resistance. Therefore, newer modes of therapeutic strategies need to be developed. Anticancer peptides (ACP) are small bioactive peptides that have the ability to be selective and toxic to cancer, with a high efficiency in cell penetration and internalization and low risk of inducing multidrug resistance. ACPs have multitude of mechanisms giving them the ability to target multiple cancer types as well as both slow growing and metabolically active cancers. Recently, a large number of literature papers examine the structure, mechanisms of action, synthesis, modifications and other non-direct cancer treatment applications of ACPs. A growing aspect in all areas of research and development especially in peptide drug discovery is now Artificial Intelligence (AI). It enables large amounts of data to be processed quickly and allows for rapid predictions hence the increased discovery of new ACPs. In this review, we discuss the structure, mechanisms of action, synthesis of ACPs and the different types of AI models, their algorithms and the ACPs prediction models currently available.