Pioneering an approach that combines simulation with machine learning, we map and model how thousands of signaling proteins interact characterizing cancer behavior at a cellular level and response or resistance to treatment.

Our platform enables us to simulate drug-like effects from compoundsthat may not exist yet, on cells potentially unavailable for lab-based testing, like those of cancer patients.

This approach will potentially allow us to predict not only what works in cells, mice and people but more importantly, why and how. Continuous iterations of simulations and proprietary in vitro and in vivo experiments confirm predictions and progress our pipeline while simultaneously improving the underlying Simulated Cell™. As all programs and partnerships run on the latest version of the in silicocell, model training benefits accumulate, leading to a constantly improving platform. Using results to both generate the initial idea and to guide its iterations, as the models improve, this leads to a more rational process to undestand the underlying disease biology. Our benchmarks show that simulations prevent 2 out of 3 failed experiments in vitro and every 2nd failure in vivo as well.