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Over 50K clinical samples
We are creating one of the largest, most robust proteomic atlas available today by harmonizing large clinical datasets as well as healthy samples. 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 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 and data science, we apply a multidisciplinary approach to predict which targets are the best candidates to further pursue.
We want to leverage dynamics and structural proteomics to precisely
discover and characterize modulators that reshape the outcome of
complex diseases.
Being modality agnostic, our platform will
utilize data across a variety of therapeutic agents.
As
such, producing an optimal therapeutic lead from the combined
space of small molecules and antibodies.
Get in touch with us for questions and potential collaborations.
