Abstract

Single-cell and spatial genomics atlases in health and disease have provided a powerful new tool for the interpretation of the human genetics of disease by placing risk alleles in their cellular and tissue context. Yet even as atlases continue to grow in scale, there is an enormous space of genetic and therapeutic possibilities, which exceeds by many orders of magnitude what can ever be tested in a lab, clinical trial, or even an entire population. Historically, this challenge was tackled by restricting the search space by prior knowledge, a practical approach which nevertheless severely limited our ability to make new discoveries and effective predictions. Instead, to augment these atlases, generative AI can make testable predictions of missing or nonexistent information, for example bridging different layers of biology, such as H&E and molecular information, helping to predict disease progression, and generating therapeutic molecules de novo or through optimization. Key to the success of this approach is an integrated interplay between data and algorithms, or a “Lab in a Loop,” where experimental or clinical data are used to train models, the models are used to predict the next set of experiments, and the process is iterated, at scale, both to yield key predictions in any specific project and improve the model for all projects. In this talk, I will describe the journey from cell and tissue atlases to therapeutic applications, the scientific basis of this approach, and how we built such a Lab in a Loop of experiments and AI in Genentech across our target discovery, drug discovery and drug development efforts to serve patients across therapeutic areas.