Computational Hit Finding: An Industry Perspective.

Journal: Journal of medicinal chemistry
Published Date:

Abstract

Computational hit finding, particularly virtual screening, has been a mainstay of drug discovery campaigns for decades, providing a cost-efficient complement to wet experiments. Innovation in this space slowed considerably as these approaches converged around mature software programs and stock chemical libraries up to ∼10 million in size. Recently, however, powered by massive increases in computational power, the emergence of ultralarge make-on-demand virtual libraries, the development of large capacity neural networks, the expansion of the domain of applicability of free energy calculations, and advances in protein structure prediction, the virtual screening field is currently seeing major change. We present a guide from industry practitioners summarizing key aspects on the changing computational hit finding landscape including practical recommendations for building a performant end-to-end screening workflow, strategies to mitigate risk by avoiding common pitfalls, determining success criteria, and a brief discussion of emerging technologies likely to impact drug discovery in the near future.

Authors

  • Paraskevi Gkeka
    Integrated Drug Discovery, Sanofi R&D, 91385 Chilly-Mazarin, France.
  • Fredrik Svensson
    Centre for Molecular Informatics, Department of Chemistry , University of Cambridge , Lensfield Road , Cambridge CB2 1EW , U.K.
  • Carlos Roca Magadán
    Galapagos SASU, 102 Avenue Gaston Roussel, 93230 Romainville, France.
  • Marcel John de Groot
    UCB, Chemin du Foriest, B-1420 Braine-l'Alleud, Belgium.
  • Steven V Jerome
    Schrödinger, Inc., 1540 Broadway, 24th Floor, New York, New York 10036, United States.