Acknowledgements: We thank S. Genapathy for helpful discussions related to GCGR/GLP-1R activation. All peptide sequence data, together with experimental potency measurements, were provided and sponsored by AstraZeneca UK Limited. A.M.P. was funded by a Raymond and Beverly Sackler Fund for Physics of Medicine (University of Cambridge), the European Research Council and the Simons Foundation. L.J.C. gratefully acknowledges support from the Simons Foundation.Funder: Simons Foundation; doi: https://doi.org/10.13039/100000893Funder: Raymond and Beverly Sackler Foundation (Raymond & Beverly Sackler Foundation Inc); doi: https://doi.org/10.13039/100013112Funder: AstraZeneca; doi: https://doi.org/10.13039/100004325Several peptide dual agonists of the human glucagon receptor (GCGR) and the glucagon-like peptide-1 receptor (GLP-1R) are in development for the treatment of type 2 diabetes, obesity and their associated complications. Candidates must have high potency at both receptors, but it is unclear whether the limited experimental data available can be used to train models that accurately predict the activity at both receptors of new peptide variants. Here we use peptide sequence data labelled with in vitro potency at human GCGR and GLP-1R to train several models, including a deep multi-task neural-network model using multiple loss optimization. Model-guided sequence optimization was used to design three groups of peptide variants, with distinct ranges of predicted dual activity. We found that three of the model-designed sequences are potent dual agonists with superior biological activity. With our designs we were able to achieve up to sevenfold potency improvement at both receptors simultaneously compared to the best dual-agonist in the training set
