SandboxAQ has announced a collaboration with Sanofi to identify biomarkers during clinical development.
The project will use SandboxAQ’s Quantitative AI models to causally filter knowledge graphs. This technique will allow scientists to automatically extract new clinical hypotheses from literature and highlight those which are truly causal. SandboxAQ’s Quantitative AI models aim to understand human biology, assisting in the identification of new biomarkers and aiding scientists’ ability to demonstrate the mechanism of action, efficacy and safety of investigational medicines and targets in clinical development.
SandboxAQ’s causal filtration models are part of their growing suite of Large Quantitative Models (LQMs), Quantitative AI models trained on multiple data streams including proprietary data generated internally by SandboxAQ algorithms. Since these data are both exact and limitless, LQMs evade the restrictions in scale and accuracy intrinsic to natural language LLM models trained on public data available on the Internet.
Nadia Harhen, the general manager of AI Simulation at Sandbox AQ, said, “Large Quantitative Models, such as those we use to causally filter through knowledge graphs, are proving impactful in many areas across the life sciences, from drug repurposing to reverse screening. The application with Sanofi focused on biomarker identification is very exciting as it increases our reach into later stage clinical development and the patient benefit that can be unlocked far beyond the early stages of drug discovery.”
Last year, SandboxAQ has announced Quantitative AI collaborations with the University of California San Francisco (UCSF), Novonix, and Riboscience. In 2024, Flagship Pioneering, SPARK NS, and other organisations signed on to further their innovation pipelines.
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