Stanford-affiliated research has been a regular source on this site for peptide chemistry, AI-driven discovery, and structural biology work. Recent threads: structural biology papers on macrocyclic peptide binding to undruggable targets, computational design platforms (often in collaboration with industry partners), and clinical work out of Stanford Medicine's metabolic-disease and oncology programs.
Programs that have appeared in this site's coverage include AI-designed peptide work in collaboration with Google DeepMind alumni, structural biology on KRAS and beta-catenin binders, and several preclinical peptide candidates moving toward IND.
Stories here cover the published work and the spinouts. See #drug-discovery and #ai-drug-discovery for adjacent threads.
A Stanford-led study published March 29 in Genome Medicine, with broad media coverage in late April, identifies two genetic variants that handicap the PAM enzyme (peptidyl-glycine alpha-amidating monooxygenase) responsible for activating GLP-1 and other peptide hormones. In a meta-analysis of three trials with 1,119 participants, carriers — roughly 10% of the general population — were less responsive to GLP-1 drugs and saw smaller HbA1c reductions despite higher circulating GLP-1 levels. The work is the first in-depth investigation of a 'GLP-1 resistance' phenotype, sits alongside the recent 23andMe GLP1R/GIPR variant paper, and opens a path toward genetically-stratified incretin prescribing.
Stanford scientists used AI to identify BRP, a naturally occurring peptide that acts directly on the hypothalamus to suppress appetite — avoiding the gut-related side effects of current GLP-1 drugs. In animal studies, BRP reduced body weight and fat without nausea, constipation, or muscle loss. Published in Nature, with human trials planned.
Stanford researchers found approximately 10% of people may have resistance to GLP-1 drugs, limiting effectiveness for glucose regulation and weight loss. The study examined individual variation in how GLP-1 drugs slow gastric emptying.
Stanford researchers developed a method converting peptide sequences into DNA for standard sequencing, published in Nature Biotechnology. Could dramatically accelerate peptide drug discovery.