Major advancements in biotech instrumentation are not just software or AI achievements. They are the result of a deeply multidisciplinary effort over many years, requiring innovations and integration across optics, fluidics, chemistry, hardware, and biology to create powerful new tools.
The bottleneck for AI in drug discovery is not the algorithm but the lack of high-quality, large-scale biological data. New platforms are needed to generate this necessary "substrate" for AI models to learn from, challenging the narrative that better models alone are the solution.
Genomic data (DNA) provides a static blueprint of potential, not a view of the actual biological activity. True understanding requires measuring the dynamic interactions of molecules and cells within tissues "downstream." Current methods capture only fragmentary slices, missing the full picture.
Historically, molecular biology, cell biology, and tissue biology were studied as separate disciplines. Spatial biology technologies like 10x Genomics' Atera platform now allow researchers to measure all three simultaneously and in context, creating a more holistic, unified view of biological systems.
The high failure rate in drug development is analogous to trying to repair a car with no mechanical knowledge—it's just "banging on different parts." This highlights the industry's need to shift from observing correlations to understanding the fundamental biological mechanisms of disease.