Applications of machine learning in drug discovery and development

  • Nat Rev Drug Discov. 2019 Jun;18(6):463-477. doi: 10.1038/s41573-019-0024-5.
Jessica Vamathevan  1 ,  Dominic Clark  2 ,  Paul Czodrowski  3 ,  Ian Dunham  4 ,  Edgardo Ferran  2 ,  George Lee  5 ,  Bin Li  6 ,  Anant Madabhushi  7  8 ,  Parantu Shah  9 ,  Michaela Spitzer  4 ,  Shanrong Zhao  10
Affiliations
  • 1. European Molecular Biology Laboratory, European Bioinformatics Institute, Cambridge, UK. [email protected].
  • 2. European Molecular Biology Laboratory, European Bioinformatics Institute, Cambridge, UK.
  • 3. Technical University of Dortmund, Dortmund, Germany.
  • 4. Open Targets and European Molecular Biology Laboratory, European Bioinformatics Institute, Cambridge, UK.
  • 5. Bristol-Myers Squibb, Princeton, NJ, USA.
  • 6. Takeda Pharmaceuticals International Co., Cambridge, MA, USA.
  • 7. Case Western Reserve University, Cleveland, OH, USA.
  • 8. Louis Stokes Cleveland Veterans Affair Medical Center, Cleveland, OH, USA.
  • 9. EMD Serono R&D Institute, Billerica, MA, USA.
  • 10. Pfizer Worldwide Research and Development, Cambridge, MA, USA.
PMID: 30976107 DOI: 10.1038/s41573-019-0024-5
Abstract

Drug discovery and development pipelines are long, complex and depend on numerous factors. Machine learning (ML) approaches provide a set of tools that can improve discovery and decision making for well-specified questions with abundant, high-quality data. Opportunities to apply ML occur in all stages of drug discovery. Examples include target validation, identification of prognostic biomarkers and analysis of digital pathology data in clinical trials. Applications have ranged in context and methodology, with some approaches yielding accurate predictions and insights. The challenges of applying ML lie primarily with the lack of interpretability and repeatability of ML-generated results, which may limit their application. In all areas, systematic and comprehensive high-dimensional data still need to be generated. With ongoing efforts to tackle these issues, as well as increasing awareness of the factors needed to validate ML approaches, the application of ML can promote data-driven decision making and has the potential to speed up the process and reduce failure rates in drug discovery and development.