Revealing the hidden biological mechanisms of disease to discover the next generation of breakthrough drugs

What if we could learn the hidden circuitry of human disease directly from patient tissue instead of cell lines or animal models or relying on fragmented and incomplete scientific literature?

What if we could go beyond discovering often spurious patterns and correlations from high-dimensional multi-omic data and instead directly learn the biological mechanisms driving disease outcomes “hidden” in the data?

What if we could simulate the efficacy and safety of a drug at the individual patient level before running clinical trials to better select the right patients?

At Aitia (pronounced “Ay-tee-ah”), derived from the Greek word for causality, we are rapidly turning this aspiration into reality through the convergence of multi-omic patient data, high-performance computing, and causal learning and AI. Our mission at Aitia is to discover the next generation of breakthrough drugs to improve outcomes for patients by creating Digital Twins of human disease from multi-omic patient data and causal AI. Gemini Digital Twins are being used today to discover novel therapies and accelerate R&D in multiple myeloma, prostate cancer, Alzheimer’s Disease, Parkinson’s Disease, and Huntington’s Disease, with several more in development across oncology, neurodegeneration, and immunology.


Aitia and Servier Announce AI Collaboration to Advance Multiple Myeloma Drug Discovery and Clinical Development

Slide 1

The case for AI-driven cancer clinical trials –
The efficacy arm in silico

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Slide 2

GNS Healthcare Announces a Multi-Year Collaboration with Memorial Sloan Kettering’s Innovation Hub to Advance Precision Medicine in Oncology

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Slide 3

GNS to Develop the World’s First in silico Patient For Prostate Cancer

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Slide 3

Listen to Dr. Ravi Parikh talk about the challenges of RCT and the GNS in silico Patient solutions

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Gemini Digital Twin
Data Partners

Gemini Digital Twin
Biopharma Partners