Core Viewpoint - The article discusses a groundbreaking study by Professor Katrin Svensson's team at Stanford University, which developed an AI system called "Peptide Predictor" that discovered 2,683 previously unknown bioactive peptides, potentially revolutionizing obesity treatment [6][8]. Group 1: AI and Drug Discovery - The AI model significantly enhances drug discovery by accurately predicting which prohormone fragments may have therapeutic potential, moving from a trial-and-error approach to a more precise method [8]. - The discovery of the BRP (BRINP2-related peptide) highlights the potential of AI in identifying effective obesity treatments, showcasing its ability to select promising candidates from a vast pool [10]. Group 2: BRP's Mechanism and Benefits - BRP demonstrated remarkable appetite suppression in animal studies, showing effects comparable to popular GLP-1 drugs, indicating its strong anti-appetite activity [10]. - The peptide also optimizes metabolic regulation, enhancing fat oxidation while maintaining stable oxygen consumption and carbon dioxide production, suggesting it primarily regulates appetite rather than basal metabolic rate [12][14]. - BRP's unique mechanism of action, which activates specific neurons in the hypothalamus, avoids common side effects associated with GLP-1 drugs, such as nausea and gastrointestinal discomfort [14]. Group 3: Future Prospects - The research team has initiated preclinical safety assessments for BRP and plans to conduct the first human trials in 2026, with hopes of bringing the drug to market by 2030 [17]. - The goal is to develop a safe and effective weight loss medication that respects the body's natural metabolic balance, representing a new direction in obesity drug development [17].