MIT chemical engineers unveiled an AI‑driven method to program industrial yeast strains for protein production that substantially reduces time and cost in biopharmaceutical manufacturing. The approach leverages large language models tuned to biological sequence and process data to predict genetic modifications that boost yield and stability. The team demonstrated accelerated optimization cycles using the model, cutting experimental burden and improving expression of therapeutic proteins in yeast. Authors contend the method can lower costs across vaccine and biologic production and compress lead development timelines. This work amplifies a broader industry push to apply machine learning to strain engineering and bioprocess optimization, promising faster scale‑up and reduced manufacturing failure risk—key levers for lowering drug development economics.