D. I. C. Wang, Marian Huot, Zechen Zhang, Kaiyi Jiang, Eugene I. Shakhnovich, Kevin M. Esvelt
Artificial intelligence now shapes the design of biological matter. Protein language models (pLMs), trained on millions of natural sequences, can predict, generate, and optimize functional proteins with minimal human input. When embedded in experimental pipelines, these systems enable closed-loop biological design at unprecedented speed. The same convergence that accelerates vaccine and therapeutic discovery, however, also creates new dual-use risks. We first map recent progress in using pLMs for fitness optimization across proteins, then critically assess how these approaches have been applied to viral evolution and how they intersect with laboratory workflows, including active learning and automation. Building on this analysis, we outline a capability-oriented framework for integrated AI-biology systems, identify evaluation challenges specific to biological outputs, and propose research directions for training- and inference-time safeguards.