Peptide Drug

Peptide drug discovery is rapidly advancing through the application of machine learning, aiming to design peptides with specific therapeutic properties more efficiently than traditional methods. Current research focuses on developing novel deep learning models, including graph neural networks, diffusion models, and protein language models, to predict peptide-protein interactions, optimize peptide sequences for desired functionalities (e.g., cell penetration), and generate novel peptide analogs with improved bioactivity. These advancements significantly accelerate the drug discovery process by reducing reliance on extensive experimental screening, ultimately leading to faster development of more effective peptide-based therapeutics.