Fifteen challenges for generative AI applications to cell biology.

Abstract

Generative AI (Gen-AI) has shown a remarkable impact in several biological research areas, from protein folding and de novo design to pathogenic mutation prediction. However, it remains unclear whether these molecular-level successes can translate to cellular and multicellular insights relevant to fields ranging from immunology to cancer and neurodegeneration. This arises from the intricate nature of the molecular mechanisms that determine cellular and organismal behavior, the lack of sufficient training data, and the multicellular nature of most pathophysiologic phenotypes. Novel Gen-AI frameworks are likely needed to integrate prior biological knowledge, such as molecular interaction networks, as well as guiding principles focusing the community's attention on solving biologically and translationally relevant problems. Drawing inspiration from Hilbert's list of 23 mathematical problems that have focused the mathematical community's attention for more than a century, we propose fifteen grand AI challenges to focus the biomedical community's attention on critically relevant questions, most of which still lack effective predictive methodologies.