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How will generative AI disrupt data science in drug discovery?

Nature Biotechnology volume 41, pages 750–751 (2023)Cite this article

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In the short few months since the release of ChatGPT1,2, the potential for large language models (LLMs) and generative artificial intelligence (AI) to disrupt fields as diverse as art, marketing, journalism, copywriting, law and software engineering is already being realized. These technologies use deep learning models trained on enormous amounts of data to generate new texts or images. While trained only to capture statistical regularities in the training data, their ability, once trained, to imitate human language in a convincing way; to generate realistic images, sounds or software; or to solve tasks apparently involving higher cognitive functions such as reasoning has caught the world by surprise. As such, they are also poised to disrupt in many ways how scientists and engineers understand biology and discover and develop new treatments.

First, existing LLMs are already able to act as extraordinary productivity tools that allow data scientists and engineers, including those working on medical research and drug discovery, to do their jobs more efficiently. Solutions like GitHub Copilot and ChatGPT are being rapidly adopted by software engineering teams to write high-quality code more quickly3, and data scientists are also increasingly generating plots and drafting reports and presentations with the help of AI-based assistants. Current LLMs can also help with more technical and complex tasks, such as solving the long-standing problem of data harmonization across multiple data centers, which remains largely dependent on human data processing. In particular, one increasingly popular approach to harmonize heterogeneous multicohort datasets is to synthesize samples that belong to a missing modality or domain using generative AI to see data harmonization as a style transfer problem4. By automating and streamlining the technical procedures associated with integrating data from heterogeneous sources, LLMs and generative AI models will increase the growth of collaborative data networks, allowing AI models to be fueled by unprecedentedly large datasets. Furthermore, in addition to harmonization, synthetic data generation can solve the problem of anonymizing sensitive data when it comes with differential privacy guarantees5, thus providing a promising technical solution to grow collaborative data networks while preserving data privacy within each contributing partner6.

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References

  1. Brown, T. B. et al. In Adv. Neural Inf. Process. Syst. 33 (eds Larochelle, H. et al.) 1877–1901 (Curran Associates, 2020).

  2. Open AI. Preprint at arXiv https://doi.org/10.48550/arXiv.2303.08774 (2023).

  3. Nguyen, N. & Nadi, S. In Proc. 19th Intl Conf. Mining Software Repositories 1–5 (Association for Computing Machinery, 2022); https://doi.org/10.1145/3524842.3528470

  4. Nan, Y. et al. Inf. Fusion 82, 99–122 (2022).

    Article  PubMed  PubMed Central  Google Scholar 

  5. Gong, M., Xie, Y., Pan, K., Feng, K. & Qin, A. K. IEEE Comput. Intell. Mag. 15, 49–64 (2020).

    Article  Google Scholar 

  6. Ogier du Terrail, J. et al. Nat. Med. 29, 135–146 (2023).

    Article  CAS  PubMed  Google Scholar 

  7. Bilodeau, C., Jin, W., Jaakkola, T., Barzilay, R. & Jensen, K. F. Wiley Interdiscip. Rev. Comput. Mol. Sci. 12, e1608 (2022).

    Article  Google Scholar 

  8. Castillo-Hair, S. M. & Seelig, G. Acc. Chem. Res. 55, 24–34 (2022).

    Article  CAS  PubMed  Google Scholar 

  9. Zrimec, J. et al. Nat. Commun. 13, 5099 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Strokach, A. & Kim, P. M. Curr. Opin. Struct. Biol. 72, 226–236 (2022).

    Article  CAS  PubMed  Google Scholar 

  11. Dauparas, J. et al. Science 378, 49–56 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Shanehsazzadeh, A. et al. Preprint at bioRxiv https://doi.org/10.1101/2023.01.08.523187 (2023).

  13. Baid, G. et al. Nat. Biotechnol. 41, 232–238 (2023).

    CAS  PubMed  Google Scholar 

  14. Zhang, R., Meng-Papaxanthos, L., Vert, J. P. & Noble, W. S. J. Comput. Biol. 29, 1198–1212 (2022).

    Article  CAS  PubMed  Google Scholar 

  15. Bengio, Y., Courville, A. & Vincent, P. IEEE Trans. Pattern Anal. Mach. Intell. 35, 1798–1828 (2013).

    Article  PubMed  Google Scholar 

  16. Vaswani, A. et al. In Adv. Neural Inf. Process. Syst. 30 (eds Guyon, I. et al.) 6000–6010 (Curran Associates, 2017).

  17. Wieder, O. et al. Drug Discov. Today. Technol. 37, 1–12 (2020).

    Article  PubMed  Google Scholar 

  18. Alshahrani, M. et al. Bioinformatics 33, 2723–2730 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Taylor, R. et al. Preprint at arXiv https://doi.org/10.48550/arXiv.2211.09085 (2022).

  20. Alkaissi, H. & McFarlane, S. I. Cureus 15, e35179 (2023).

    PubMed  PubMed Central  Google Scholar 

  21. Mitchell, M. & Krakauer, D. C. Preprint at arXiv https://doi.org/10.48550/arXiv.2210.13966 (2023).

  22. Mialon, G. et al. Preprint at arXiv https://doi.org/10.48550/arXiv.2302.07842 (2023).

  23. Weidinger, L. et al. In FAccT '22: 2022 ACM Conf. Fairness, Accountability, and Transparency 214–229 (Association for Computing Machinery, 2022); https://doi.org/10.1145/3531146.3533088

  24. Mitchell, M. et al. In Proc. Conf. Fairness, Accountability, and Transparency 220–229 (Association for Computing Machinery, 2019); https://doi.org/10.1145/3287560.3287596

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    Jean-Philippe Vert

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Vert, JP. How will generative AI disrupt data science in drug discovery?. Nat Biotechnol 41, 750–751 (2023). https://doi.org/10.1038/s41587-023-01789-6

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