Increase your laboratory success rate and reduce
time to trial with AI guided simulated experiments

Discovery of new cancer drugs is urgently needed, yet going too slow.
It’s time to make it as efficient as it should be.

The current model of cancer drug discovery is based on decades-old methods. Cell line and animal experiments still use an approach of trial and error. Due to the lack of a reliable tool that predicts a compound's mechanism of action, researchers have to run multiple rounds of laboratory experiments to make predictions about a drug's effects. Turbine can add valuable insights into how a drug works from the preclinical phase through phases I and II to reach phase III trials faster, and with a reduced failure rate.

The trial and error approach, combined with the slow and costly biological models, is not predictive enough in the current paradigm of targeted medicine, leading a to a historic low of oncology research ROI in the past decade. Turbine’s in silico experiments can test an almost infinite number of variables in a model that reflects the biology of cancerous cells accurately. By including interactions of cellular signalling pathways and genetic mutations, Turbine’s model accounts for the vast complexity of human cells. Turbine takes laboratory trial and error out of drug discovery, and onto our servers. The result is a more focused, more rational, and years shorter process. Additionally, results of the simulations help understand treatments’ exact mechanism of action, while uncovering biomarkers for patient selection and maximum clinical benefit.