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Computational drug discovery

Machine learning trims the peptide drug design process to a sweet spot

Drugs that target peptide hormone receptors are of great interest in the treatment of type 2 diabetes. In spite of limited data and vast design spaces, a bespoke computational pipeline has designed peptides that target two receptors with high potency.

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Fig. 1: Computational pipeline to slim down the peptide design space and discover novel potent GCGR/GLP-1R dual agonists.

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Correspondence to Daniel Reker.

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Competing interests

D. R. acts as a consultant to the pharmaceutical and biotechnology industry, as a mentor for Start2, and serves on the scientific advisory board of Areteia Therapeutics. C. E. M. declares no competing interests.

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Markey, C.E., Reker, D. Machine learning trims the peptide drug design process to a sweet spot. Nat. Chem. 16, 1394–1395 (2024). https://doi.org/10.1038/s41557-024-01610-0

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