3,661
Views
0
CrossRef citations to date
0
Altmetric
Editorial

Can artificial intelligence accelerate preclinical drug discovery and precision medicine?

, ORCID Icon, ORCID Icon & ORCID Icon
Pages 661-665 | Received 04 Feb 2022, Accepted 13 Jun 2022, Published online: 19 Jun 2022

References

  • Wouters OJ, McKee M, Luyten J. Estimated research and development investment needed to bring a new medicine to market, 2009-2018. JAMA. 2020;323(9):844–853.
  • Bender A, Cortés-Ciriano I. Artificial intelligence in drug discovery: what is realistic, what are illusions? Part 1: ways to make an impact, and why we are not there yet. Drug Discov Today. 2021;26(2):511–524.
  • Boniolo F, Dorigatti E, Ohnmacht AJ, et al., Artificial intelligence in early drug discovery enabling precision medicine. Expert Opin Drug Discov. 16(9): 991–1007. 2021.
  • de Azevedo WF. Application of machine learning techniques for drug discovery. Curr Med Chem. 2021;28(38):7805–7807.
  • McCulloch WS, Pitts W. A logical calculus of the ideas immanent in nervous activity. Bull Math Biophys. 1943;5(4):115–133.
  • Wójcikowski M, Siedlecki P, Ballester PJ. Building machine-learning scoring functions for structure-based prediction of intermolecular binding affinity. Methods Mol Biol. 2019;2053:1–12.
  • Vamathevan J, Clark D, Czodrowski P, et al., Applications of machine learning in drug discovery and development. Nat Rev Drug Discov. 18(6): 463–477. 2019.
  • Fröhlich H, Balling R, Beerenwinkel N, et al. From hype to reality: data science enabling personalized medicine. BMC Med. 2018;16(1):150.
  • Roses AD. Pharmacogenetics in drug discovery and development: a translational perspective. Nat Rev Drug Discov. 2008;7(10):807–817.
  • Slamon DJ, Clark GM, Wong SG, et al., Human breast cancer: correlation of relapse and survival with amplification of the HER-2/neu oncogene. Science. 235(4785): 177–182. 1987.
  • Davies H, Bignell GR, Cox C, et al., Mutations of the BRAF gene in human cancer. Nature. 417(6892): 949–954. 2002.
  • Iorio F, Knijnenburg TA, Vis DJ, et al., A landscape of pharmacogenomic interactions in cancer. Cell. 166(3): 740–754. 2016.
  • Gao H, Korn JM, Ferretti S, et al. High-throughput screening using patient-derived tumor xenografts to predict clinical trial drug response. Nat Med. 2015;21(11):1318–1325.
  • Garnett MJ, Edelman EJ, Heidorn SJ, et al., Systematic identification of genomic markers of drug sensitivity in cancer cells. Nature. 483(7391): 570–575. 2012.
  • Quaranta M, Knapp B, Garzorz N, et al. Intraindividual genome expression analysis reveals a specific molecular signature of psoriasis and eczema. Sci Transl Med. 2014;6(244):244ra90–244ra90.
  • Schaebitz A, Hillig C, Farnoud A, et al. Low numbers of cytokine transcripts drive inflammatory skin diseases by initiating amplification cascades in localized epidermal clusters. bioRxiv. 2021. https://www.biorxiv.org/content/ https://doi.org/10.1101/2021.06.10.447894v1.
  • Cancer Cell Line Encyclopedia Consortium and Genomics of Drug Sensitivity in Cancer Consortium. Pharmacogenomic agreement between two cancer cell line data sets. Nature. 2015;528(7580):84–87.
  • Turki T, Wei Z, Wang JTL. Transfer learning approaches to improve drug sensitivity prediction in multiple Myeloma patients. IEEE Access. 2017;5:7381–7393.
  • Rampášek L, Hidru D, Smirnov P, et al. Dr.VAE: improving drug response prediction via modeling of drug perturbation effects. Bioinformatics. 2019;35(19):3743–3751.
  • Chang Y, Park H, Yang H, et al. Cancer Drug Response Profile scan (CDRscan): a deep learning model that predicts drug effectiveness from cancer genomic signature. Sci Rep. 2018;8(1):8857.
  • Manica M, Oskooei A, Born J, et al. Toward explainable anticancer compound sensitivity prediction via multimodal attention-based convolutional encoders. Mol Pharm. 2019;16(12):4797–4806.
  • Menden MP, Wang D, Mason MJ, et al., Community assessment to advance computational prediction of cancer drug combinations in a pharmacogenomic screen. Nat Commun. 10(1): 2674. 2019.
  • Konečný J, McMahan HB, Yu FX, et al. Federated learning: strategies for improving communication efficiency. arXiv Preprint arXiv;2017. https://arxiv.org/abs/1610.05492
  • Warnat-Herresthal S, Schultze H, Lingadahalli Shastry K, et al., Swarm learning for decentralized and confidential clinical machine learning. Nature. 594(7862): 265–270. 2021.

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.