1574-Fifteen Challenges for Generative AI in Cell Biology
This perspective outlines fifteen grand challenges for applying generative AI to cellular and multicellular biology, moving beyond molecular-level successes like protein folding. The authors argue that current models, often based on large language model architectures, face structural hurdles including data scarcity and the immense combinatorial complexity of biological networks. To overcome these limitations, they suggest incorporating biological priors—such as physical laws and molecular interaction graphs—directly into AI frameworks. The proposed roadmap spans four hierarchical levels…
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