See the future of your drug in a
Virtual Cell.
Predict, validate, improve and succeed.
Why Drug Discovery
Fails
Current approaches fail to capture the mechanistic basis of human biology, limiting our ability to understand disease processes and patient responses to treatment.
Black-box biology
We lack technologies that can truly open the black box of human biology and provide mechanistic insight into cellular behavior. Bulk assays average cellular responses and obscure rare but critical cell populations that often drive resistance and treatment failure.
Fragmented, proxy-based readouts
Most approaches focus on a single layer of information. Even single-cell technologies primarily measure RNA, which is a proxy for cellular state rather than a direct readout of function. scRNA-only methods capture potential activity, not actual cellular function.
Trial-and-error development
Without structured, multi-dimensional data generated at scale, drug discovery remains driven by trial and error rather than predictive, biology-driven models. Because fundamental biology relies on interconnected molecular layers and temporal dynamics, multi-omic measurement is essential for an accurate, functional view of cellular behaviour and for enabling in silico testing before moving to costly experimental validation.
Frequently Asked Questions
What is single-cell technology?
What is the difference between single-cell and bulk experiments?
What does “single-cell multi-omics” mean?
Why is single-cell multi-omics more powerful than a single-omic approach?
Ready to Decode
Black Box Biology?
Partner with Quriegen to leverage high-fidelity multi-omics data and AI simulations for your drug discovery pipeline.
