We use machine learning to unlock the power of the vast and expanding biomedical data landscape to generate new insights into the underlying causes of disease.
Generating a 360° view of disease biology
Massive quantities of biomedical data are generated daily, yet scientists currently only use a fraction of the information available to understand the causes of diseases and propose new treatments.
We provide scientists with a holistic view of disease biology by integrating as much data as possible from across domains and data types, including ’omics, molecules, experimental data, literature, pathology and biological systems – totalling over 85 data sources. By bringing together these disparate, complex data sources, we break down silos across therapeutic areas to connect shared mechanisms across diseases.
Turning data into knowledge
Our automated, custom-engineered processing pipelines make this disparate data accessible and useful for scientific enquiry. Machine learning models extract biomedical entities, such as genes, diseases, drugs, processes and cell types, and infer relationships that capture how these entities interact in a human system.
These relationships are stored in our Knowledge Graph as a network of contextualised scientific facts – providing a proprietary integrated view of biomedical data that supports discovery and decision making.
The data foundation for our AI drug discovery Platform
Scientists use AI tools to access the data in the graph; they can explore disease networks and associated mechanisms, proteins or treatments to define and refine their hypotheses and guide the drug discovery process.
Discovering a leading COVID-19 treatment
Our comprehensive data strategy allows us to work on any disease, or even pivot to tackle urgent public health issues. In early 2020, BenevolentAI scientists used our Knowledge Graph and tools to uncover a COVID-19 treatment1, which is now approved by the US FDA.2