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  • Review Article
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Designing nanotheranostics with machine learning

Nature Nanotechnology volume 19, pages 1769–1781 (2024)Cite this article

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Abstract

The inherent limits of traditional diagnoses and therapies have driven the development and application of emerging nanotechnologies for more effective and safer management of diseases, herein referred to as ‘nanotheranostics’. Although many important technological successes have been achieved in this field, widespread adoption of nanotheranostics as a new paradigm is hindered by specific obstacles, including time-consuming synthesis of nanoparticles, incomplete understanding of nano–bio interactions, and challenges regarding chemistry, manufacturing and the controls required for clinical translation and commercialization. As a key branch of artificial intelligence, machine learning (ML) provides a set of tools capable of performing time-consuming and result-perception tasks, thus offering unique opportunities for nanotheranostics. This Review summarizes the progress and challenges in this emerging field of ML-aided nanotheranostics, and discusses the opportunities in developing next-generation nanotheranostics with reliable datasets and advanced ML models to offer better clinical benefits to patients.

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Fig. 1: Workflow for ML-aided nanotheranostics.
Fig. 2: Timeline of key milestones for ML and nanotheranostics.
Fig. 3: ML-assisted synthesis of nanoparticles.
Fig. 4: ML-assisted understanding of nano–bio interactions.
Fig. 5: ML-assisted nanotheranostic applications.

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Acknowledgements

This work was supported by National Natural Science Foundation of China (82222035 and 82372106), Shenzhen Medical Research Fund (B2302041), Shenzhen Bay Laboratory Proof-of-Concept Fund (S231801005), National Medical Research Council of Singapore (MOH-001388-00 and CG21APR1005), Singapore Ministry of Education (MOE-000387-00) and National University of Singapore (NUHSRO/2021/034/TRP/09/Nanomedicine).

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Authors and Affiliations

  1. Shenzhen Bay Laboratory, Shenzhen, China

    Lang Rao

  2. Computer Science and Artificial Intelligence Lab, Massachusetts Institute of Technology, Cambridge, MA, USA

    Yuan Yuan

  3. Department of Computer Science, Boston College, Chestnut Hill, MA, USA

    Yuan Yuan

  4. Tencent AI Lab, Shenzhen, China

    Xi Shen

  5. Intellindust, Shenzhen, China

    Xi Shen

  6. Key Laboratory of Bioorganic Phosphorus Chemistry and Chemical Biology, Department of Chemistry, Tsinghua University, Beijing, China

    Guocan Yu

  7. Departments of Diagnostic Radiology, Surgery, Chemical and Biomolecular Engineering, and Biomedical Engineering, Yong Loo Lin School of Medicine and Faculty of Engineering, National University of Singapore, Singapore, Singapore

    Xiaoyuan Chen

  8. Clinical Imaging Research Centre, Centre for Translational Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore

    Xiaoyuan Chen

  9. Nanomedicine Translational Research Program, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore

    Xiaoyuan Chen

  10. Theranostics Center of Excellence (TCE), Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore

    Xiaoyuan Chen

  11. Institute of Molecular and Cell Biology, Agency for Science, Technology and Research (A*STAR), Singapore, Singapore

    Xiaoyuan Chen

Authors
  1. Lang Rao
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  2. Yuan Yuan
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  3. Xi Shen
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  4. Guocan Yu
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  5. Xiaoyuan Chen
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Contributions

L.R. contributed the idea and completed the figures. Y.Y., X.S., G.Y. and X.C. helped to write, review and revise the paper.

Corresponding authors

Correspondence to Lang Rao or Xiaoyuan Chen.

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Competing interests

X.S. is the co-founder and CTO of Shenzhen Intellindust. X.C. is the co-founder and CTO of Yantai LNC Biotechnology. The other authors declare no competing interests.

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Nature Nanotechnology thanks Milad Abolhasani, Liangfang Zhang and Hao Zhu for their contribution to the peer review of this work.

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Rao, L., Yuan, Y., Shen, X. et al. Designing nanotheranostics with machine learning. Nat. Nanotechnol. 19, 1769–1781 (2024). https://doi.org/10.1038/s41565-024-01753-8

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