Skip to main content

Advertisement

SpringerLink
Log in

Opportunities and challenges using artificial intelligence in ADME/Tox

  • Comment
  • Published:

From Nature Materials

View current issue Submit your manuscript

At the recent Artificial Intelligence Applications in Biopharma Summit in Boston, USA, a panel of scientists from industry who work at the interface of machine learning and pharma discussed the diverging opinions on the past, present and future role of AI for ADME/Tox in drug discovery and development.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

References

  1. Gupta, R. R. et al. Drug Metab. Dispos. 38, 2083–2090 (2010).

    Article  CAS  Google Scholar 

  2. Ekins, S., Honeycutt, J. D. & Metz, J. T. Drug Discov. Today 15, 451–460 (2010).

    Article  CAS  Google Scholar 

  3. Page, K. M. Mol. Pharm. 13, 609–620 (2016).

    Article  CAS  Google Scholar 

  4. Webborn, P. J. H. Future Med. Chem. 6, 1233–1235 (2014).

    Article  CAS  Google Scholar 

  5. Zientek, M. et al. Chem. Res. Toxicol. 23, 664–676 (2010).

    Article  CAS  Google Scholar 

  6. Zhang, H. et al. Toxicol. In Vitro 23, 134–140 (2009).

    Article  CAS  Google Scholar 

  7. Butler, K. T., Davies, D. W., Cartwright, H., Isayev, O. & Walsh, A. Nature 559, 547–555 (2018).

    Article  CAS  Google Scholar 

  8. Wang, S. et al. Mol. Pharm. 13, 2855–2866 (2016).

    Article  CAS  Google Scholar 

  9. Ekins, S. & Williams, A. J. Lab Chip 10, 13–22 (2010).

    Article  CAS  Google Scholar 

  10. Winiwarter, S. et al. J. Comput. Aided Mol. Des. 29, 795–807 (2015).

    Article  CAS  Google Scholar 

  11. Clark, A. M., Williams, A. J. & Ekins, S. J. Cheminform. 7, 9 (2015).

    Article  Google Scholar 

  12. Martin, E. J., Polyakov, V. R., Tian, L. & Perez, R. C. J. Chem. Inf. Model. 57, 2077–2088 (2017).

    Article  CAS  Google Scholar 

  13. Ericksen, S. S. et al. J. Chem. Inf. Model. 57, 1579–1590 (2017).

    Article  CAS  Google Scholar 

  14. Verras, A. et al. J. Chem. Inf. Model. 57, 445–453 (2017).

    Article  CAS  Google Scholar 

  15. Capuzzi, S. J. et al. J. Chem. Inf. Model. 57, 105–108 (2017).

    Article  CAS  Google Scholar 

  16. Sushko, I. et al. J. Comput. Aided Mol. Des. 25, 533–554 (2011).

    Article  CAS  Google Scholar 

  17. Russo, D. P., Zorn, K. M., Clark, A. M., Zhu, H. & Ekins, S. Mol. Pharm. 15, 4361–4370 (2018).

    Article  CAS  Google Scholar 

  18. Sheridan, R. P. J. Chem. Inf. Model. 53, 2837–2850 (2013).

    Article  CAS  Google Scholar 

  19. Roy, K., Kar, S. & Ambure, P. Chemom. Intell. Lab. Syst. 145, 22–29 (2015).

    Article  CAS  Google Scholar 

  20. Ma, J., Sheridan, R. P., Liaw, A., Dahl, G. E. & Svetnik, V. J. Chem. Inf. Model. 55, 263–274 (2015).

    Article  CAS  Google Scholar 

  21. Liu, K. et al. Preprint at https://arxiv.org/abs/1803.06236 (2018).

  22. Ramsundar, B. et al. J. Chem. Inf. Model. 57, 2068–2076 (2017).

    Article  CAS  Google Scholar 

  23. Hop, P., Allgood, B. & Yu, J. Mol. Pharm. 15, 4371–4377 (2018).

    Article  CAS  Google Scholar 

  24. Rodríguez-Pérez, R. & Bajorath, J. ACS Omega 3, 12033–12040 (2018).

    Article  Google Scholar 

  25. Korotcov, A., Tkachenko, V., Russo, D. P. & Ekins, S. Mol. Pharm. 14, 4462–4475 (2018).

    Article  Google Scholar 

  26. Lane, T. et al. Mol. Pharm. 15, 4346–4360 (2018).

    Article  CAS  Google Scholar 

  27. Xu, Y., Ma, J., Liaw, A., Sheridan, R. P. & Svetnik, V. J. Chem. Inf. Model. 57, 2490–2504 (2017).

    Article  CAS  Google Scholar 

  28. Ramsundar, B. et al. Preprint at https://arxiv.org/abs/1502.02072 (2015).

  29. Kearnes, S., McCloskey, K., Berndl, M., Pande, V. & Riley, P. J. Comput. Aided Mol. Des. 30, 595–608 (2016).

    Article  CAS  Google Scholar 

  30. Ekins, S. Pharm. Res. 33, 2594–2603 (2016).

    Article  CAS  Google Scholar 

Download references

Acknowledgements

D. Chipman and E. Cutler are kindly acknowledged for organizing the AI Applications in Biopharma Summit. S.E. acknowledges A. Clark, J. Freundlich and A. Williams for their many discussions on machine learning and ADME/Tox models. S.E. acknowledges funding to Collaborations Pharmaceuticals Inc. from NIGMS R44 GM122196-02A1.

Author information

Authors and Affiliations

  1. Novartis Institutes for Biomedical Research, Cambridge, MA, USA

    Barun Bhhatarai

  2. Relay Therapeutics, Cambridge, MA, USA

    W. Patrick Walters

  3. Genentech, South San Francisco, CA, USA

    Cornelis E. C. A. Hop

  4. Numerate, San Francisco, CA, USA

    Guido Lanza

  5. Collaborations Pharmaceuticals Inc., Raleigh, NC, USA

    Sean Ekins

Authors
  1. Barun Bhhatarai
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. W. Patrick Walters
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Cornelis E. C. A. Hop
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Guido Lanza
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Sean Ekins
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Sean Ekins.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Bhhatarai, B., Walters, W.P., Hop, C.E.C.A. et al. Opportunities and challenges using artificial intelligence in ADME/Tox. Nat. Mater. 18, 418–422 (2019). https://doi.org/10.1038/s41563-019-0332-5

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41563-019-0332-5

  • Springer Nature Limited

This article is cited by

Search

Navigation