The AI-Native Biologist

Artificial intelligence can predict protein structures, analyze cells, generate molecules, and synthesize vast bodies of research. But the central scientific challenge is not generating more possibilities. It is deciding what those possibilities mean, when to trust them, and what experiment should come next.

The AI-Native Biologist is a plain-language guide to thinking clearly about AI-generated evidence in modern biology. Rather than teaching code or a particular software platform, Adrian Keller focuses on principles that outlast rapidly changing tools: how biological reality becomes data, how representations shape what models can learn, why confident predictions can still fail, how shortcut learning and distribution shift mislead, and how experiments turn computational possibilities into scientific knowledge.

Through examples from protein structure, single-cell biology, imaging, drug discovery, generative models, active learning, and automated laboratories, the book develops a practical framework - the Bio-AI Canvas - for moving from questions and measurements to predictions, uncertainty, checks, and better experiments.

For biologists, life scientists, students, and curious readers who want to work productively with AI while remaining anchored in evidence.