The fusion of artificial intelligence and omics: a perspective toward 2035.

Abstract

Artificial intelligence (AI) has catalyzed a revolution in the life sciences, culminating in AlphaFold's capacity to predict the static structure of proteins with atomic precision. However, this static view-analogous to a molecular radiograph-omits the fundamental dimension of protein dynamics, a critical factor for biological function and drug interaction. The phenomenon of induced fit, whereby proteins reconfigure upon ligand binding, remains largely beyond the reach of current models. This article posits that the next frontier in computational biology lies in the adoption of World Models-AI architectures such as Google DeepMind's Genie 3 and Meta AI's Joint-Embedding Predictive Architecture. Rather than predicting individual states, these models learn the underlying rules governing system evolution. We argue that applying these principles to molecular dynamics may enable the simulation of conformational trajectories, predicting how proteins move, flex, and respond to ligand binding. We explore the transformative implications for oncology, particularly in the design of allosteric modulators and in targeting historically "undruggable" proteins such as intrinsically disordered proteins. Finally, we delineate a roadmap toward 2035, highlighting challenges and opportunities at the convergence of generative AI and dynamic structural biology.