AAGP integrates physicochemical and compositional features for machine learning-based prediction of anti-aging peptides | Scientific Reports

Researchers developed AAGP, a machine learning tool designed to predict potential anti-aging peptides. By analyzing over 4,000 physicochemical and compositional features, the model achieved high accuracy in distinguishing anti-aging peptides from other random or antimicrobial sequences. This computational approach aims to accelerate the discovery of peptide therapies for age-related decline.