Human intelligence and technology united to re-engineer drug discovery and deliver life-changing medicines.
Our knowledge pipeline pulls data from various structured and unstructured biomedical data sources and curates and standardises this knowledge via a data fabric.
This is fed into our proprietary knowledge graph which extracts and contextualises the relevant information and is made up of a vast number of machine curated relationships between diseases, genes, drugs.
Relation inference AI models help us predict potential disease targets that may be overlooked by scientists. Our gene expression-based models help us to identify proteins that are differentially expressed in healthy and diseased cells. This data-driven TargetID method increases hypothesis volume and results in high quality target choices.
Commonly, diseases have been defined by symptoms or location in the body, not by their underlying patient-specific molecular mechanisms or pathways. Our applied machine learning models enable scientists to determine the right mechanism to modulate, and identify patient endotypes most likely to respond to treatment.
The chemical space for exploration is infinitely vast and only a small fraction of it can potentially be made into medicines. Our AI-augmented models empower chemists to evaluate millions of molecular structures, generate drug-like molecules and design better drugs in fewer cycles.
We recognise that no one business can revolutionise the way medicines are discovered and developed on their own. We want to leverage our technology and expertise in partnership with the world’s leading researchers and scientists.
We have built a talented and diverse team from backgrounds across biology, chemistry, machine learning and engineering, all of us driven by a belief that no patient should suffer without treatment.Meet the team