Literature DB >> 27878643

The evolution of drug design at Merck Research Laboratories.

Frank K Brown1, Edward C Sherer2, Scott A Johnson3, M Katharine Holloway4, Bradley S Sherborne5.   

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.

Entities:  

Keywords:  CADD; Modeling; Predictive sciences

Mesh:

Substances:

Year:  2016        PMID: 27878643     DOI: 10.1007/s10822-016-9993-1

Source DB:  PubMed          Journal:  J Comput Aided Mol Des        ISSN: 0920-654X            Impact factor:   3.686


  22 in total

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2.  Contemporary Computational Applications and Tools in Drug Discovery.

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  4 in total

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