The evolution of Alzheimer's target identification: Towards a fusion of artificial and cellular intelligence

Decades of advances unfolding in parallel across diverse domains have delivered to science rapid rises in the scale of multiplexing, population-level cohort sizes, global computational capacity, massive-scale artificial intelligence (AI) models, and advanced human cellular modeling capabilities. These have generated unprecedented volumes of data, allowing researchers to explore Alzheimer's disease (AD) biology at a depth and scale never before possible. The explosion of multi-omics datasets and computational power heralds an era in which the complexity of AD can be meaningfully dissected and reconstructed leveraging AI. These can be applied to advance our understanding of the root causes of disease, fundamentally a forward problem, tracing how dysfunction emergence from interactions across genes, cells and environments over time. On the other hand, therapeutic discovery requires addressing the inverse problem, working back from the diseased state to pinpoint upstream interventions that restore health. Human induced pluripotent stem cells (iPSCs) and other human cell models play a pivotal role in this process, naturally computing the mapping from perturbation to phenotype at scale. By recreating human-relevant biology, this cellular intelligence enables validation of targets predicted by AI and testing of interventions that drive therapeutic progress. We look to the next horizon in Alzheimer's research as a collaboration, a convergence of three forms of intelligence: human, artificial and cellular. In unison, these complementary forces will shape a new frontier for AD research where scientific innovation and human ingenuity work together bringing hope for meaningful advances and new therapies.