Machine‐Learning‐Enabled Rapid Evolution of Photoenzymes for the Asymmetric Synthesis of <i>gem</i> ‐Difluorophosphonates

Abstract

ABSTRACT gem ‐Difluorophosphonates are pivotal structural motifs in pharmaceuticals and bioactive molecules. While photoenzymatic catalysis provides a powerful platform to overcome the challenges of enantioselective synthesis, engineering enzymes for non‐natural transformations remains an arduous, labor‐intensive process. Although predictive methods utilizing protein language models (PLMs) offer fitness landscape guidance, they often struggle to generalize across diverse protein families or accurately map sequence to catalytic activity. Here, we report a small‐sample, accelerated evolution strategy that integrates focused rational iterative site‐specific mutagenesis (FRISM) with the EVOLVEpro model. This synergistic approach identifies high‐activity and enantiospecific variants through structure‐based hotspot identification and active learning, requiring minimal experimental throughput. By screening only 40 variants over three evolutionary rounds, we identified four beneficial mutations whose combinations enable the synthesis of diverse fluorinated products with up to &gt; 99% yield and 98:2 enantiomeric ratio (e.r.)—a 65% reduction in workload compared to exhaustive screening. Mechanistic investigations suggest an electron donor‐acceptor (EDA)‐complex‐free radical addition pathway, terminated by the flavin semiquinone (FMN sq ) or the active‐site residue Y343. This study provides a robust, “lightweight” machine learning framework for the rapid development of new‐to‐nature photoenzymatic transformations.