How AI is accelerating the development of GCGR/GLP-1R dual agonists with enhanced biological potency
In the global battle against obesity and type 2 diabetes, scientists are developing increasingly sophisticated weapons. While drugs like semaglutide (Wegovy) and tirzepatide (Zepbound) have revolutionized treatment, researchers continue to push boundaries by creating multi-targeting peptides that simultaneously activate multiple metabolic receptors. The latest breakthrough comes from an unexpected alliance: artificial intelligence and pharmacology are joining forces to design next-generation dual agonists with unprecedented precision and effectiveness.
Machine learning accelerates peptide optimization
Simultaneously target multiple metabolic pathways
Up to 7x improvement in biological activity
The most promising candidates in this new class of therapeutics are GCGR/GLP-1R dual agonists—single molecules that activate both the glucagon receptor (GCGR) and glucagon-like peptide-1 receptor (GLP-1R). This powerful combination harnesses the complementary benefits of both pathways: GLP-1R activation reduces appetite and improves blood sugar control, while GCGR activation increases energy expenditure, creating a dual attack on obesity through both reduced calorie intake and enhanced calorie burning 1 7 .
The rationale for dual agonism stems from understanding the limitations of single-target approaches. GLP-1 receptor agonists work primarily by reducing energy intake—they slow gastric emptying and promote satiety, helping people eat less. However, long-term weight management requires addressing both sides of the energy equation: intake and expenditure 1 .
This is where glucagon receptor activation provides a crucial advantage. Glucagon naturally increases energy expenditure, making it an ideal partner to GLP-1's appetite-suppressing effects 1 7 . The combination has been described as "hitting the metabolic system with a one-two punch"—reducing food intake while simultaneously increasing calorie burning.
Designing effective dual agonists is exceptionally challenging. These peptides must maintain high potency at both receptors while achieving the appropriate balance of activity between them. The relationship between peptide sequence and functional activity is complex and not fully understood, making traditional trial-and-error approaches time-consuming and expensive 2 .
In a groundbreaking study published in Nature Chemistry, researchers demonstrated that machine learning could dramatically accelerate the design of optimized GCGR/GLP-1R dual agonists 2 . The research team faced a significant hurdle: with limited experimental data available, could they train models accurate enough to predict the activity of new peptide variants?
The team assembled a training dataset of 125 peptide variants with known potency measurements at both GCGR and GLP-1R. These peptides contained varying numbers of mutations—from as few as two to as many as twenty modifications from the wild-type human glucagon sequence 2 . Each peptide was labeled with its experimental EC50 values (the concentration needed to achieve half-maximal response) at both human GCGR and GLP-1R.
The researchers compared several different machine learning architectures, ultimately finding that an ensemble of multi-task convolutional neural network (CNN) models achieved the best performance 2 . This approach had several innovative aspects:
The model simultaneously learned to predict potency against both receptors, allowing it to leverage shared patterns in the data.
The team trained multiple copies of the model and averaged their predictions, reducing uncertainty and increasing robustness.
Peptides were represented using simple one-hot encoding of their amino acid sequences, allowing the model to learn directly from primary structure.
The model was trained using a six-fold cross-validation scheme, with 105 sequences for training, 10 for validation, and 10 as a held-out test set for each fold 2 . This rigorous approach ensured the model could generalize to unseen peptide sequences rather than merely memorizing the training data.
To test their trained models, the researchers designed a prospective experiment: using the model to design entirely new peptide sequences with specific activity profiles 2 . They employed a simple optimization strategy to design 15 novel peptides—five in each of three distinct activity profiles:
Potency primarily at glucagon receptor
Potency primarily at GLP-1 receptor
High potency at both receptors
The experimental workflow followed these steps:
The results were striking: three of the model-designed sequences emerged as potent dual agonists with superior biological activity compared to the best dual agonist in the original training set 2 . The most successful designs achieved up to a sevenfold potency improvement at both receptors simultaneously—a significant advance that would be challenging to achieve through traditional methods.
| Peptide Design | Predicted GCGR Potency (log10EC50) | Actual GCGR Potency (log10EC50) | Predicted GLP-1R Potency (log10EC50) | Actual GLP-1R Potency (log10EC50) |
|---|---|---|---|---|
| Best in Training | -12.08 (0.83 pM) | -12.08 (0.83 pM) | -11.50 (3.19 pM) | -11.50 (3.19 pM) |
| Model Design 1 | -12.42 | -12.65 (0.22 pM) | -11.89 | -12.10 (0.79 pM) |
| Model Design 2 | -12.35 | -12.52 (0.30 pM) | -11.92 | -11.98 (1.05 pM) |
| Model Design 3 | -12.28 | -12.41 (0.39 pM) | -11.85 | -11.87 (1.35 pM) |
This demonstrated the model's ability to navigate the complex sequence-activity relationship and identify non-obvious mutations that enhanced potency at both receptors. The successful prediction of multiple high-performing peptides also validated the robustness of the machine learning approach.
The development and testing of GCGR/GLP-1R dual agonists relies on a sophisticated array of research tools and assays. These reagents enable scientists to characterize the binding, pharmacology, and functional activity of novel peptide candidates.
| Research Tool | Function | Application in Dual Agonist Research |
|---|---|---|
| cAMP Gs Assays | Measures G-protein coupled receptor activation through cAMP production | Primary assay for determining functional potency at GCGR and GLP-1R 5 |
| IP-One Assay | Detects inositol phosphate accumulation | Accounts for secondary Gq-coupling exhibited by most primarily Gs-coupled receptors 5 |
| Beta-Arrestin Recruitment Assay | Measures receptor internalization and biased signaling | Characterizes additional pharmacological properties beyond G-protein signaling 5 |
| Tag-lite Binding Assay | Non-radioactive real-time binding measurement | Investigates ligand binding kinetics to cell-surface receptors 5 |
| Cell Lines Expressing GCGR/GLP-1R | Engineered cells with specific receptor expression | Standardized platforms for consistent potency measurements 1 2 |
| Primary Hepatocytes | Liver cells with natural receptor expression | Validates activity in physiologically relevant systems 1 |
The successful application of machine learning to design GCGR/GLP-1R dual agonists represents a paradigm shift in peptide therapeutics. This approach could significantly accelerate the drug discovery process, reducing both time and costs while potentially identifying more effective therapeutic candidates.
Similar methodologies could be applied to design multi-targeting peptides for other complex diseases where single-target approaches have shown limitations.
As structural biology provides increasingly detailed views of peptide-receptor interactions, these data can further refine machine learning models.
While challenges remain—including the need for larger training datasets and more sophisticated model architectures—the fusion of artificial intelligence and pharmaceutical science has unquestionably opened new frontiers in the development of life-changing metabolic therapeutics.
The marriage of machine learning and peptide drug development marks an exciting advancement in the fight against obesity and metabolic disease. By leveraging algorithms to navigate the complex landscape of peptide sequence-activity relationships, researchers have designed novel GCGR/GLP-1R dual agonists with enhanced potency that could lead to more effective treatments. As this field evolves, we can anticipate a new era of intelligent drug design where computational approaches work hand-in-hand with experimental science to develop increasingly sophisticated therapies for some of humanity's most pressing health challenges.