To develop new and better peptides, the short amino-acid strings behind medicines like GLP-1 drugs, researchers have used AI to generate candidates and to predict their properties.
However, merging these capabilities into a system that generates peptides likely to activate or block specific targets has proven difficult. In part, this is due to the vast number of possible peptides, but also because predicting how readily a peptide will bind to a target — like G protein-coupled receptors (GPCRs), a family of cell-surface proteins targeted by about one-third of approved drugs — is easier than simultaneously forecasting what effect that binding will have.
Now, researchers at the University of Pennsylvania and The Chinese University of Hong Kong have created TD3B, an AI framework that guides peptide generation toward candidates predicted to have a desired effect. The results, which focus on GPCRs, are described in a paper presented as a Spotlight at the 2026 International Conference on Machine Learning.
The advance could accelerate drug development by making it easier to design peptide drug candidates likely to have particular effects, such as improving GLP-1-related therapies for weight loss or diabetes, quieting brain signals involved in addiction or helping immune cells fight cancer, rather than generating new candidates first and testing them later to discover what they do.
