ArticleNature chemistry2024
Machine learning designs new GCGR/GLP-1R dual agonists with enhanced biological potency.
Article in Nature chemistry, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
18 citing papers in PubMed.
- Identification of chemical features for improved outer membrane permeation in mycobacteria using machine learning.Nature microbiology · 2026Article
- Engineering Marker-Free Lettuce Chloroplast Genome to Express Functional Glucagon-Like Peptide-1 Receptor Agonists Exenatide and Lixisenatide.Plant biotechnology journal · 2026Article
- Learning physical interactions to compose biological large language models.Communications chemistry · 2026Review
- Artificial intelligence in obesity management: clinical evidence, translational gaps, and implementation priorities-a structured narrative review.Frontiers in endocrinology · 2026Review
- Balancing metabolic optimization and reproductive safety in Polycystic Ovary Syndrome: a Bayesian-informed framework for GLP-1 receptor agonists.Frontiers in nutrition · 2026Review
- GPCR Biased Signaling in Metabolism.Handbook of experimental pharmacology · 2026Review
- Progress of AI-Driven Drug-Target Interaction Prediction and Lead Optimization.International journal of molecular sciences · 2025Review
- AI-Driven De Novo Design of Ultra Long-Acting GLP-1 Receptor Agonists.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- ProDualNet: dual-target protein sequence design method based on protein language model and structure model.Briefings in bioinformatics · 2025Article
- Exogenous oral application of PYY and exendin-4 impacts upon taste-related behavior and taste perception in wild-type mice.Neuropharmacology · 2025Article
- Artificial intelligence to improve cardiovascular population health.European heart journal · 2025Review
- A modular artificial intelligence framework to facilitate fluorophore design.Nature communications · 2025Article
- Anti-obesity peptides from food: Production, evaluation, sources, and commercialization.Comprehensive reviews in food science and food safety · 2025Review
- Ligand-Induced Biased Activation of GPCRs: Recent Advances and New Directions from In Silico Approaches.Molecules (Basel, Switzerland) · 2025Review
- Glucagon in metabolic disease: a mini-review of emerging multi-organ roles beyond glycemic control.Frontiers in endocrinology · 2025Review
- Machine learning-guided optimization of triple agonist peptide therapeutics for metabolic disease.Frontiers in bioinformatics · 2025Article
- A Review ofFoods (Basel, Switzerland) · 2024Review
- Therapeutic peptides and their delivery using lipid-based nanoparticles.Tzu chi medical journalReview
Corrections and comments
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Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Several 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.
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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.