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Exploiting machine learning for end-to-end drug discovery and development

Nature Materials volume 18, pages 435–441 (2019)Cite this article

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Abstract

A variety of machine learning methods such as naive Bayesian, support vector machines and more recently deep neural networks are demonstrating their utility for drug discovery and development. These leverage the generally bigger datasets created from high-throughput screening data and allow prediction of bioactivities for targets and molecular properties with increased levels of accuracy. We have only just begun to exploit the potential of these techniques but they may already be fundamentally changing the research process for identifying new molecules and/or repurposing old drugs. The integrated application of such machine learning models for end-to-end (E2E) application is broadly relevant and has considerable implications for developing future therapies and their targeting.

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Fig. 1: Implementing end-to-end (E2E) machine learning models at all stages of drug discovery and development illustrating some of the key areas that could be modelled.
Fig. 2: Demonstrating iterative drug discovery using machine learning.

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Acknowledgements

In memory of Rebecca J. Williams. J. Freundlich, R. J. G. Arnold, P. Madrid, J. Lage de Siqueira-Neto, A. Williams, A. Tropsha, A. Gerlach, J. Gerlach, D. Chipman, A. Davidow and M. Hupcey are kindly acknowledged for discussions and some of the collaborations described herein. S.E. acknowledges funding to Collaborations Pharmaceuticals, Inc., from NIGMS R44 GM122196-02A1, NINDS 1R43NS107079-01, NINDS 3R43NS107079-01S1, NCATS 1UH2TR002084-01 and FY2018 UNC Research Opportunities Initiative (ROI) award. Research reported in this publication was supported by the National Institute of Neurological Disorders and Stroke of the National Institutes of Health under award number R43NS107079. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

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

    Sean Ekins, Ana C. Puhl, Kimberley M. Zorn, Thomas R. Lane, Daniel P. Russo & Jennifer J. Klein

  2. The Rutgers Center for Computational and Integrative Biology, Camden, NJ, USA

    Daniel P. Russo

  3. RTI International, Research Triangle Park, NC, USA

    Anthony J. Hickey

  4. UNC Catalyst for Rare Diseases, Eshelman School of Pharmacy, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA

    Anthony J. Hickey

  5. Molecular Materials Informatics, Inc., Montreal, Quebec, Canada

    Alex M. Clark

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S.E. is founder and CEO, A.C.P., K.M.Z., T.L. and J.J.K. are employees, and D.P.R. and A.M.C. are consultants of Collaborations Pharmaceuticals, Inc. A.M.C. is also the founder and owner of Molecular Materials Informatics, Inc. A.J.H. has no conflicts of interest.

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Ekins, S., Puhl, A.C., Zorn, K.M. et al. Exploiting machine learning for end-to-end drug discovery and development. Nat. Mater. 18, 435–441 (2019). https://doi.org/10.1038/s41563-019-0338-z

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