Make undruggable targets a thing ofthe past
The current pharmaceutical research and development model is broken and unsustainable.
With the current pharmaceutical R&D model, it takes about 12 to 15 years to bring a new drug to market.
The estimated cost of research and development sits at over $2.5 billion per drug approval, increasing at a rate of 8.5% each year.
More than 90% of drug candidates that enter Phase 1 clinical trials fail to reach regulatory approval, often due to the limited predictive value of preclinical models of disease.
Drug approvals remain stagnant in the current model. Fewer than 70 new drugs enter the market each year. With such a low yield, scalability is nearly impossible.
To decrease failure rates and increase R&D productivity, we must take more shots at a variety of disease-causing targets and therapeutic modalities.
Using machine learning, our multi-module drug discovery platforms generate more novel drug candidates, shorten the timeline, cut operational costs and increase the probability of success of pharmaceutical R&D.
One important distinction: we believe AI and computational technologies are impactful in drug discovery only when biology is our North Star. Our platforms combine domain expertise in biology and pharmacology with AI and machine learning to provide an end-to-end solution.