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Journal of Computer-Aided Molecular Design

, Volume 31, Issue 3, pp 255–266 | Cite as

The evolution of drug design at Merck Research Laboratories

  • Frank K. BrownEmail author
  • Edward C. Sherer
  • Scott A. Johnson
  • M. Katharine Holloway
  • Bradley S. Sherborne
Article

Abstract

On October 5, 1981, Fortune magazine published a cover article entitled the “Next Industrial Revolution: Designing Drugs by Computer at Merck”. With a 40+ year investment, we have been in the drug design business longer than most. During its history, the Merck drug design group has had several names, but it has always been in the “design” business, with the ultimate goal to provide an actionable hypothesis that could be tested experimentally. Often the result was a small molecule but it could just as easily be a peptide, biologic, predictive model, reaction, process, etc. To this end, the concept of design is now front and center in all aspects of discovery, safety assessment and early clinical development. At present, the Merck design group includes computational chemistry, protein structure determination, and cheminformatics. By bringing these groups together under one umbrella, we were able to align activities and capabilities across multiple research sites and departments. This alignment from 2010 to 2016 resulted in an 80% expansion in the size of the department, reflecting the increase in impact due to a significant emphasis across the organization to “design first” along the entire drug discovery path from lead identification (LID) to first in human (FIH) dosing. One of the major advantages of this alignment has been the ability to access all of the data and create an adaptive approach to the overall LID to FIH pathway for any modality, significantly increasing the quality of candidates and their probability of success. In this perspective, we will discuss how we crafted a new strategy, defined the appropriate phenotype for group members, developed the right skillsets, and identified metrics for success in order to drive continuous improvement. We will not focus on the tactical implementation, only giving specific examples as appropriate.

Keywords

Modeling Predictive sciences CADD 

Notes

Acknowledgments

The work described has been enabled by a diverse group of computer scientists, informaticians, modelers, crystallographers, chemists, biologists, etc. who are too numerous to name individually. We thank all of our collaborators over the years. However, we would specifically like to call attention to those who made significant contributions toward building the computational infrastructure (i.e. Rich Bach, Gene Fluder, Joe Forbes, Simon Kearsley, Dennis Schuchman, Joe Shpungin), to some of those who provided scientific leadership in the design group (e.g. Christopher Bayly, Wendy Cornell, Chris Culberson, Meir Glick, Daniel McMasters, Steve Soisson, Corey Strickland, Johannes Voigt, and Chris Waller), and to Mike Rowley who primed the organization in recent years for a sea change in the use of design tools.

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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Frank K. Brown
    • 1
    Email author
  • Edward C. Sherer
    • 2
  • Scott A. Johnson
    • 3
  • M. Katharine Holloway
    • 1
  • Bradley S. Sherborne
    • 4
  1. 1.Structural Chemistry DepartmentMRL Research LaboratoriesWest PointUSA
  2. 2.Structural Chemistry DepartmentMRL Research LaboratoriesRahwayUSA
  3. 3.Structural Chemistry DepartmentMRL Research LaboratoriesBostonUSA
  4. 4.Structural Chemistry DepartmentMRL Research LaboratoriesKenilworthUSA

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