What we do
We believe in biological plausibility and faithful representations.
The core of our technology is based on extracting the semantics that govern biological interactions using machine learning and massive amounts of public and proprietary data.
Our state-of-the-art machine learning algorithms utilize multi-omic data structures to create causal representations of the biological events leading to disease onset.
Not only are our models accurate and comprehensive, they are also transparent, providing explainable predictions and allowing researchers to investigate specific pathways that define disease and health.
Simulations of physiological events are further supported by real world evidence from existing clinical trials. The Simmunome platform is also structured to integrate new emerging data to ensure we stay at the forefront of cutting-edge research.
- Simulations of the modulation caused by a specific drug in the disease network, giving high confidence probabilities for success/failure of a drug candidate.
- Predictions of clinical trial outcomes to de-risk clinical development programs and reduce the likelihood of late-stage failures.
- Our simulations are validated against past trials in Alzheimer’s disease and Melanoma with nearly 100% prediction accuracy.
- Identifying and validating novel molecules to target against disease based on our in-silico models. The comprehensive and complex disease models we design allow reliable target identification and validation.
- We map human physiological systems and diseases at a molecular level, capturing mechanisms of development for each disease.
- Allowing the user to investigate diagnostics and possible mechanisms of therapeutics promptly and reliably. This opens the door to answer questions that are difficult to answer in the lab.