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Artificial intelligence–powered biofoundries for protein engineering and metabolic engineering

  • Junyu Chen
  • , Nilmani Singh
  • , Jingxia Lu
  • , Stephan T. Lane
  • , Huimin Zhao

Research output: Contribution to journalArticlepeer-review

Abstract

Synthetic biology is rapidly evolving through the integration of artificial intelligence (AI) and automated biofoundries. This convergence accelerates the design–build–test–learn cycle, shifting protein engineering and metabolic engineering from labor-intensive manual experimentation to autonomous experimentation. This review summarizes recent advances in workflow development, AI models, and their integration with biofoundries for automated or autonomous protein engineering and metabolic engineering. Particularly, we highlight the potential of AI-powered biofoundries for accelerated scientific discovery and innovation in synthetic biology.

Original languageEnglish (US)
Article number103380
JournalCurrent Opinion in Biotechnology
Volume96
DOIs
StatePublished - Dec 2025

ASJC Scopus subject areas

  • Biotechnology
  • Bioengineering
  • Biomedical Engineering

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