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Thousands of clinical samples
We have created the largest, most robust proteomic atlas available today by harmonizing large clinical datasets as well as healthy samples from various organs and indications. This provides us with a baseline for downstream pattern recognition of specific causal targets to modulate.
Leveraging mass spectrometry proteomics and other protein-oriented large scale datasets, we are building machine learning models to identify up to 1,000,000 proteo-forms including post-translational-modifications, protein-protein-interactions, quantification and different structural populations of proteins.
After identifying the proteome of both healthy and diseased samples, we prioritize the higher value targets for in-vitro and in-vivo validation. Utilizing biology, chemistry and machine learning, we apply a multidisciplinary approach to predict which targets are the best candidates to further pursue.
We leverage dynamics and structural proteomics AI to precisely discover and characterize modulators that reshape the outcome of complex diseases.
Get in touch with us for questions and potential collaborations.
