Zephyr's machine learning models seamlessly integrate clinical and molecular patient features with drug structures and targets, enabling the prediction of drug response and the identification of gene networks critical to tumor survival.
We begin by transforming or encoding complex data from each modality into rich, numerical, ML-ready representations.
These representations are fed into a neural network architecture optimized for the dual tasks of predicting drug response and identifying critical Vulnerability Networks™.
Our joint model is trained on millions of drug and functional screen experiments and has been validated retrospectively in complex cohorts of Real World Data.
While our foundational models generate novel drug-specific signatures and corresponding biological rationales that validate in complex Real World Data cohorts, it is sometimes beneficial to tailor these models to specific use cases.
For partners seeking highly optimized models for their drug, Zephyr's platform offers a 'fine-tuning' process. This involves starting with our original model and adjusting its parameters using partner-specific preclinical or clinical data, along with the drug's structure.
Zephyr possesses one of the largest clinicogenomic datasets in the world, including over 68 million longitudinal patient records updated at regular intervals. Our foundational AI models leverage the vast scale and diversity to extract orthogonal signals and augment sparse datasets for optimal insight generation.
Partner with us to accelerate the journey from discovery to patient impact.
Connect with us!