ArticleJournal of medicinal chemistry2024
Machine-Learning-Guided Peptide Drug Discovery: Development of GLP-1 Receptor Agonists with Improved Drug Properties.
Article in Journal of medicinal chemistry, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Computationally Evidence-Grounded Sequence-First Design of Peptide Binders.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Targeting the Undruggable: Deep Learning-Driven Design of Peptide Therapeutics in Cancer.Pharmaceuticals (Basel, Switzerland) · 2026Review
- INBAdvanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- AHR/NRF2 Dual Agonist Prediction and Natural Compound Screening Based on Machine Learning: A New Strategy for the Treatment of Atopic Dermatitis.International journal of molecular sciences · 2026Article
- Enhancing Peptide Hydrophilicity of SPPS-Derived Peptides Using Fmoc Noncanonical Amino Acids: A Review.ACS biomaterials science & engineering · 2026Review
- Next-Generation Artificial Intelligence for ADME Prediction in Drug Discovery: From Small Molecules to Biologics.Yonago acta medica · 2026Review
- Peptide-based drug design using generative AI.Chemical communications (Cambridge, England) · 2026Review
- Peptide-based therapeutics targeting GPCRs: recent applications in the treatment of metabolic disorders.Frontiers in pharmacology · 2026Review
- The expanding role of artificial intelligence in personalised medicine: from innovation to individualized care.Frontiers in medicine · 2026Review
- Artificial intelligence in obesity management: clinical evidence, translational gaps, and implementation priorities-a structured narrative review.Frontiers in endocrinology · 2026Review
- Pipeline for development of acylated peptide based CGRP receptor antagonist with extended half-life for migraine treatment.Scientific reports · 2025Article
Corrections and comments
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Authors and funding
13 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Peptide-based drug discovery has surged with the development of peptide hormone-derived analogs for the treatment of diabetes and obesity. Machine learning (ML)-enabled quantitative structure-activity relationship (QSAR) approaches have shown great promise in small molecule drug discovery but have been less successful in peptide drug discovery due to limited data availability. We have developed a peptide drug discovery platform called streaMLine, enabling rigorous design, synthesis, screening, and ML-driven analysis of large peptide libraries. Using streaMLine, this study systematically explored secretin as a peptide backbone to generate potent, selective, and long-acting GLP-1R agonists with improved physicochemical properties. We synthesized and screened a total of 2688 peptides and applied ML-guided QSAR to identify multiple options for designing stable and potent GLP-1R agonists. One candidate, GUB021794, was profiled in vivo (S.C., 10 nmol/kg QD) and showed potent body weight loss in diet-induced obese mice and a half-life compatible with once-weekly dosing.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.