Our platform combines artificial intelligence with proven peptide discovery concepts to create a next-generation cyclic peptide drug discovery engine. By integrating deep learning models with high-throughput mRNA display screening, we can explore vast libraries of macrocyclic peptides incorporating both canonical and non-canonical amino acids. The AI models predict binding affinity, selectivity, and pharmacokinetic properties, while wet-lab validation ensures rapid iteration and optimization. This hybrid approach dramatically accelerates the discovery of hit compounds and their evolution into therapeutic candidates.
Leveraging hit peptides from our AI-enhanced platform for three distinct therapeutic modalities.
Starting from AI-validated hit peptides, we optimize pharmacological properties using structure-based design and non-natural amino acid incorporation. For oral delivery challenges, we leverage structural data to guide the conversion of peptides into small molecule mimetics via computational chemistry.
Our platform designs high-specificity carrier peptides for targeted delivery of diverse payloads: radionuclides, oligonucleotides, or cytotoxic agents. AI predicts conjugation sites and linker stability to optimize pharmacokinetics and minimize off-target effects.
By tethering peptides with different target specificities, we create multispecific therapeutics that address complex disease mechanisms. AI models predict optimal linker architectures and synergistic effects, enabling single-molecule combinations that act as bispecific or trispecific agents.
Our platform combines three core engines to seamlessly integrate in silico design with wet-lab experimentation.
Generative AI models trained on peptide-protein interaction data create focused libraries enriched for high binding affinity and structural diversity. These virtual libraries incorporate non-canonical amino acids and macrocyclic scaffolds, drastically reducing the experimental search space.
Deep learning models predict binding modes, affinities, and selectivities against target proteins. Hits are virtually evolved through iterative in silico mutagenesis, exploring sequence space that would be impossible to cover experimentally. Top candidates are then synthesized and screened via our automated mRNA display system.
AI-selected candidates are synthesized and screened using our high-throughput mRNA display platform. Binding data, structural information (X-ray, Cryo-EM), and functional assays are fed back into the AI models to refine predictions, creating a rapid closed-loop optimization cycle.
Accelerating the discovery of next-generation peptide therapeutics through intelligent design and validation.
From target nomination to optimized lead series: a seamless partnership model.
We discuss your therapeutic goals, target biology, and desired modality (peptide, PDC, MPC) to define the optimal AI-driven discovery strategy.
Our AI models generate focused virtual libraries and predict top hits. We refine predictions based on your input (e.g., desired properties, structural data).
Top AI candidates are synthesized and screened via our automated mRNA display platform. Hits are confirmed, and binding kinetics/structural data are generated.
Experimental data feeds back into AI models for subsequent rounds. Optimized leads are characterized, and we deliver comprehensive reports for further development or partnership.
Flexible engagement tailored to your project stage and strategic goals.
Common questions about our AI-driven cyclic peptide platform and partnership models.
Contact us today to discuss how our integrated platform can accelerate your peptide drug discovery.
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