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<p>Self-driving laboratories, which incorporate robotics, advanced sensing, automated feedback control and artificial intelligence, are revolutionizing the conduct of biopharmaceutical research. By coupling high-throughput experimentation with real-time data analytics, these autonomous systems enable rapid exploration of chemical space and adaptive optimization that far outpaces conventional workflows. Here, in collaboration with the Enabling Technologies Consortium, this Perspective highlights the growing importance of self-driving labs for biopharmaceutical discovery and development, drawing on recent industrial applications in solubility screening, solid-form characterization, electrochemical synthesis and lipid nanoparticle formulation. We describe the operational workflow of self-driving laboratories, including key hardware and software components that underpin closed-loop experimentation, and emphasize the critical role of machine learning in guiding experimental design and extracting mechanistic insights from complex data streams. Current barriers, including limited sensor compatibility, data format heterogeneity and the need for integration across diverse instruments, are examined, and strategies for overcoming these barriers are proposed. We envision a future in which continuously learning, data-driven laboratories become foundational infrastructure for accelerating drug development and improving reproducibility across the biopharmaceutical industry.</p><p></p>

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Towards self-driving laboratories in the biopharmaceutical industry

  • 期刊论文

  • 发表日期：

    2026年8月4日

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  • Samagra Dvivedi,
  • Nitin Minocha,
  • Shuang Chen,
  • Beata Chertok,
  • Daniel Estabrook,
  • Yuchen Fan,
  • Suyong Han,
  • Deepak Jain,
  • Manish S. Kelkar,
  • Alison Lui,
  • Nandkishor K. Nere,
  • Phenil J. Patel,
  • Kenneth G. Rodriguez,
  • Eric R. Sacia,
  • Priyanka G. Singh,
  • Dimitri Skliar,
  • Emad I. Wafa,
  • Meenesh R. Singh

摘要

Self-driving laboratories, which incorporate robotics, advanced sensing, automated feedback control and artificial intelligence, are revolutionizing the conduct of biopharmaceutical research. By coupling high-throughput experimentation with real-time data analytics, these autonomous systems enable rapid exploration of chemical space and adaptive optimization that far outpaces conventional workflows. Here, in collaboration with the Enabling Technologies Consortium, this Perspective highlights the growing importance of self-driving labs for biopharmaceutical discovery and development, drawing on recent industrial applications in solubility screening, solid-form characterization, electrochemical synthesis and lipid nanoparticle formulation. We describe the operational workflow of self-driving laboratories, including key hardware and software components that underpin closed-loop experimentation, and emphasize the critical role of machine learning in guiding experimental design and extracting mechanistic insights from complex data streams. Current barriers, including limited sensor compatibility, data format heterogeneity and the need for integration across diverse instruments, are examined, and strategies for overcoming these barriers are proposed. We envision a future in which continuously learning, data-driven laboratories become foundational infrastructure for accelerating drug development and improving reproducibility across the biopharmaceutical industry.