Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Comment
  • Published:

Artificial intelligence foundation for therapeutic science

Nature Chemical Biology volume 18, pages 1033–1036 (2022)Cite this article

Subjects

Artificial intelligence (AI) is poised to transform therapeutic science. Therapeutics Data Commons is an initiative to access and evaluate AI capability across therapeutic modalities and stages of discovery, establishing a foundation for understanding which AI methods are most suitable and why.

This is a preview of subscription content, access via your institution

Relevant articles

Open Access articles citing this article.

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Overview of Therapeutics Data Commons.
Fig. 2: AI-ready datasets, machine learning tasks and benchmarks in Therapeutics Data Commons.
Fig. 3: Example use cases of Therapeutics Data Commons.

References

  1. Pushpakom, S. et al. Nat. Rev. Drug Discovery 18, 41–58 (2019).

    Article  CAS  PubMed  Google Scholar 

  2. Macarron, R. et al. Nat. Rev. Drug Discovery 10, 188–195 (2011).

    Article  CAS  PubMed  Google Scholar 

  3. Gao, W., Raghavan, P. & Coley, C. W. Nat. Commun. 13, 1–4 (2022).

    CAS  Google Scholar 

  4. LeCun, Y., Bengio, Y. & Hinton, G. Nature 521, 436–444 (2015).

    Article  CAS  PubMed  Google Scholar 

  5. Vamathevan, J. et al. Nat. Rev. Drug Discovery 18, 463–477 (2019).

    Article  CAS  PubMed  Google Scholar 

  6. Stokes, J. M. et al. Cell 180, 688–702 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Gysi, D. M. et al. Proc. Natl Acad. Sci. USA 118, e2025581118 (2021).

    Article  CAS  Google Scholar 

  8. Jumper, J. et al. Nature 596, 583–589 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Schneider, P. et al. Nat. Rev. Drug Discov. 19, 353–364 (2020).

    Article  CAS  PubMed  Google Scholar 

  10. Wilkinson, M. D. et al. Sci. Data 3, 1–9 (2016).

    Article  Google Scholar 

  11. Chandrasekaran, S. N., Ceulemans, H., Boyd, J. D. & Carpenter, A. E. Nat. Rev. Drug Discov. 20, 145–159 (2021).

    Article  CAS  PubMed  Google Scholar 

  12. Sanchez-Lengeling, B. & Aspuru-Guzik, A. Science 361, 360–365 (2018).

    Article  CAS  PubMed  Google Scholar 

  13. Walters, W. P. & Murcko, M. Nat. Biotechnol. 38, 143–145 (2020).

    Article  CAS  PubMed  Google Scholar 

  14. Brown, N., Fiscato, M., Segler, M. H. & Vaucher, A. C. J. Chem. Inf. Model. 59, 1096–1108 (2019).

    Article  CAS  PubMed  Google Scholar 

  15. Gao, W. & Coley, C. W. J. Chem. Inf. Model. 60, 5714–5723 (2020).

    Article  CAS  PubMed  Google Scholar 

  16. Graff, D. E., Shakhnovich, E. I. & Coley, C. W. Chem. Sci. 12, 7866–7881 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Zhavoronkov, A. et al. Nat. Biotechnol. 37, 1038–1040 (2019).

    Article  CAS  PubMed  Google Scholar 

  18. Townshend, R. J. et al. Science 373, 1047–1051 (2021).

    Article  CAS  PubMed  Google Scholar 

  19. Hodgson, J. Nat. Biotechnol. 19, 722–726 (2001).

    Article  CAS  PubMed  Google Scholar 

  20. Zagidullin, B. et al. Nucleic Acids Res 47, W43–W51 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Öztürk, H., Özgür, A. & Ozkirimli, E. Bioinformatics 34, i821–i829 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  22. Huang, K. et al. Preprint at https://doi.org/10.48550/arXiv.2010.03951 (2020).

  23. Urbina, F., Lentzos, F., Invernizzi, C. & Ekins, S. Nat. Mach. Intell. 4, 189–191 (2022).

    Article  Google Scholar 

  24. Gilmer, J., Schoenholz, S. S., Riley, P. F., Vinyals, O. & Dahl, G. E. In Proc. 34th International Conference on Machine Learning (eds Precup, D. & Teh, Y. W.) 70, 1263–1272 (2017).

  25. Xie, Y. et al. In Proc. 9th International Conference on Learning Representations (Spotlight Proceedings) https://openreview.net/forum?id=kHSu4ebxFXY (2021).

Download references

Acknowledgements

K.H. and M.Z. gratefully acknowledge the support of US National Science Foundation (NSF) awards IIS-2030459 and IIS-2033384, US Air Force contract no. FA8702-15-D-0001 and awards from the Harvard Data Science Initiative, Amazon Research, Bayer Early Excellence in Science, AstraZeneca Research and the Roche Alliance with Distinguished Scientists. W.G. was supported by the US Office of Naval Research under grant no. N00014-21-1-2195. C.W.C. was supported by the Machine Learning for Pharmaceutical Discovery and Synthesis (MLPDS) Consortium. J.S. was supported by NSF awards SCH-2014438, IIS-1838042, US National Institutes of Health (NIH) award 1R01NS107291-01, and OSF Healthcare. J.L. was supported by the US Defense Advanced Research Progress Agency under awards HR00112190039, N660011924033; the Army Research Organization under nos. W911NF-16-1-0342, W911NF-16-1-0171; the NSF under nos. OAC-1835598, OAC-1934578, CCF-1918940; the NIH under no. 3U54HG010426-04S1; and the Stanford Data Science Initiative, the Wu Tsai Neurosciences Institute, Amazon, Docomo, GSK, Hitachi, Intel, JPMorgan Chase, Juniper Networks, KDDI, NEC and Toshiba. Any opinions, findings, conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the funders.

Author information

Author notes

  1. Kexin Huang

    Present address: Department of Computer Science, Stanford University, Stanford, CA, USA

  2. These authors contributed equally: Kexin Huang, Tianfan Fu, Wenhao Gao

Authors and Affiliations

  1. Health Data Science Program, Harvard University, Boston, MA, USA

    Kexin Huang

  2. College of Computing, Georgia Institute of Technology, Atlanta, GA, USA

    Tianfan Fu

  3. Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA

    Wenhao Gao & Connor W. Coley

  4. Heinz College, Carnegie Mellon University, Pittsburgh, PA, USA

    Yue Zhao

  5. School of Medicine, Stanford University, Stanford, CA, USA

    Yusuf Roohani

  6. Department of Computer Science, Stanford University, Stanford, CA, USA

    Jure Leskovec

  7. Analytics Center of Excellence, IQVIA, Cambridge, MA, USA

    Cao Xiao

  8. Department of Computer Science, University of Illinois at Urbana-Champaign, Champaign, IL, USA

    Jimeng Sun

  9. Department of Biomedical Informatics, Harvard Medical School, Harvard University, Boston, MA, USA

    Marinka Zitnik

  10. Broad Institute of MIT and Harvard, Cambridge, MA, USA

    Marinka Zitnik

  11. Harvard Data Science Initiative, Cambridge, MA, USA

    Marinka Zitnik

Authors
  1. Kexin Huang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Tianfan Fu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Wenhao Gao
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Yue Zhao
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Yusuf Roohani
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Jure Leskovec
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Connor W. Coley
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Cao Xiao
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Jimeng Sun
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Marinka Zitnik
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

K.H., T.F., W.G. and M.Z. designed the data management and computational infrastructure. K.H., T.F., W.H., Y.Z., Y.R. and M.Z. implemented the programming interface and software package. K.H., T.F., W.H. and Y.R. retrieved, processed and harmonized datasets. K.H. and M.Z. designed and implemented the website. K.H., T.F., W.H., Y.Z., Y.R., J.L., C.C, C.X., J.S. and M.Z. wrote and edited the manuscript. M.Z. conceived and supervised the study.

Corresponding author

Correspondence to Marinka Zitnik.

Ethics declarations

Competing interests

The authors declare no competing interests.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Huang, K., Fu, T., Gao, W. et al. Artificial intelligence foundation for therapeutic science. Nat Chem Biol 18, 1033–1036 (2022). https://doi.org/10.1038/s41589-022-01131-2

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41589-022-01131-2

This article is cited by

Search

Advanced search

Quick links

Nature Briefing: Translational Research

Sign up for the Nature Briefing: Translational Research newsletter — top stories in biotechnology, drug discovery and pharma.

Get what matters in translational research, free to your inbox weekly. Sign up for Nature Briefing: Translational Research