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This week, we sit down with Jean-Baptiste Morlot of Deep Life, who makes the case that the drug discovery field has been asking the wrong thing of its AI models. Correlation-based machine learning can find patterns, but it can't tell you why a cell is sick or what it would take to make it healthy again. Jean-Baptiste walks us through how single-cell sequencing unlocked the data volume needed to train something far more ambitious: a causal world model of cellular biology that learns the underlying rules of disease rather than memorizing them.
We get into how Deep Life's TwinCell platform works, why most virtual cell benchmarks are quietly inflated by popularity bias, and what it means to build an interpretable AI that a biologist can trust.
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