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: molecular interactions, molecular function, cellular systems, and clinical translation. By establishing rigorous prospective benchmarks and community-led validation efforts, the researchers aim to transition AI from retrospective statistical exercises to a tool for actionable biological discovery and improved human health.References:Dupire L, Khan A A, Karaletsos T, et al. Fifteen challenges for generative AI applications to cell biology[J]. Cell, 2026.前往小宇宙评论区与主播互动