Revealing toxicity. In every cell.
We predict and explain how drugs cause toxicity, before they reach patients.
Drug toxicity remains one of the biggest challenges in drug development
Most drug candidates fail during development. Many fail because toxicity signals appear too late to act. Earlier insight means safer and more affordable drugs.
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A new way to understand cellular response
DeepCyte combines single-cell metabolomics with AI to detect metabolic changes within individual cells. This predicts toxicity and reveals mechanisms that remain invisible to conventional approaches.
Our mission is to go beyond prediction — to reveal why drugs damage cells and deliver the mechanistic evidence that guides better decisions — before any patient is exposed.
Explainable Predictive Toxicology
Our vision is to put the 'why' into every prediction. We are doing so by building Digital Cells, human-first AI models that can predict toxicity and highlight mechanisms behind toxic effects, providing actionable information that can help make drugs safer.
Our enabling technology is world-leading high-throughput single-cell metabolomics, built on over 10+ years of work in leading academic institutions. It provides unprecedented resolution, sensitivity, and throughput.
By detecting shifts in metabolic markers, identifying characteristic metabolic patterns, and mapping drug-induced changes into metabolic networks, our AI-based solutions connect a prediction to its biological cause.
DeeImmuno is DeepCyte's first product. It goes beyond a toxicity risk score — it shows you what is happening inside immune cells, and why.
- Predict toxicity types in immune cells at sublethal concentrations
- Deconvolve co-existing toxicity types triggered by the same compound
- Reveal mechanisms and metabolic rewiring triggered by drugs
- Deliver actionable mechanistic insights, not just a risk score
Our award-winning team brings together leaders in science and business, with a track record of building field-defining products and companies.
Theodore Alexandrov
Shawn Owens
Henry Wong
Carl Evertsz
Gina Wallbank
Jeany Delafiori
