Over the past decades, protein engineering has matured into a field of its own, driven by computational modeling and high-throughput wet lab experiments, with broad application in therapeutics, diagnostics, agriculture, and manufacturing. In recent years, artificial intelligence (AI) has further propelled protein engineering by enabling more efficient search through high-dimensional sequence space for proteins with desired properties. Notable AI-based advances encompass generative modeling of sequences, backbone structure, and atoms; tailoring general versions of such models to design proteins with specific properties; modeling for extraction of protein representations and scoring candidate protein sequences; and developing techniques for library design, including synthesis-aware approaches. Herein we discuss these advances, emphasizing a unifying view through a statistical interpretation of modern AI approaches.
Listgarten et al. (2026) studied this question.