Literature DB >> 26409840

The impact of data integrity on decision making in early lead discovery.

Bernd Beck1, Daniel Seeliger2, Jan M Kriegl2.   

Abstract

Data driven decision making is a key element of today's pharmaceutical research, including early drug discovery. It comprises questions like which target to pursue, which chemical series to pursue, which compound to make next, or which compound to select for advanced profiling and promotion to pre-clinical development. In the following paper we will exemplify how data integrity, i.e. the context data is generated in and auxiliary information that is provided for individual result records, can influence decision making in early lead discovery programs. In addition we will describe some approaches which we pursue at Boehringer Ingelheim to reduce the risk for getting misguided.

Keywords:  Assay interference; Data integrity; Data management; Decision making; False positives; Lead discovery; Reporting; Sample properties; Screening

Mesh:

Year:  2015        PMID: 26409840     DOI: 10.1007/s10822-015-9871-2

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


  24 in total

Review 1.  Measurement of [Ca2+] using the Fluorometric Imaging Plate Reader (FLIPR).

Authors:  E Sullivan; E M Tucker; I L Dale
Journal:  Methods Mol Biol       Date:  1999

2.  Development of a virtual screening method for identification of "frequent hitters" in compound libraries.

Authors:  Olivier Roche; Petra Schneider; Jochen Zuegge; Wolfgang Guba; Manfred Kansy; Alexander Alanine; Konrad Bleicher; Franck Danel; Eva-Maria Gutknecht; Mark Rogers-Evans; Werner Neidhart; Henri Stalder; Michael Dillon; Eric Sjögren; Nader Fotouhi; Paul Gillespie; Robert Goodnow; William Harris; Phil Jones; Mikio Taniguchi; Shinji Tsujii; Wolfgang von der Saal; Gerd Zimmermann; Gisbert Schneider
Journal:  J Med Chem       Date:  2002-01-03       Impact factor: 7.446

3.  A common mechanism underlying promiscuous inhibitors from virtual and high-throughput screening.

Authors:  Susan L McGovern; Emilia Caselli; Nikolaus Grigorieff; Brian K Shoichet
Journal:  J Med Chem       Date:  2002-04-11       Impact factor: 7.446

4.  New substructure filters for removal of pan assay interference compounds (PAINS) from screening libraries and for their exclusion in bioassays.

Authors:  Jonathan B Baell; Georgina A Holloway
Journal:  J Med Chem       Date:  2010-04-08       Impact factor: 7.446

5.  Screening-based translation of public research encounters painful problems.

Authors:  Jonathan B Baell
Journal:  ACS Med Chem Lett       Date:  2015-02-09       Impact factor: 4.345

6.  How experimental errors influence drug metabolism and pharmacokinetic QSAR/QSPR models.

Authors:  Mark C Wenlock; Lars A Carlsson
Journal:  J Chem Inf Model       Date:  2014-12-24       Impact factor: 4.956

7.  Quantification of frequent-hitter behavior based on historical high-throughput screening data.

Authors:  J Willem M Nissink; Sam Blackburn
Journal:  Future Med Chem       Date:  2014-06       Impact factor: 3.808

8.  Matched molecular pair analysis: significance and the impact of experimental uncertainty.

Authors:  Christian Kramer; Julian E Fuchs; Steven Whitebread; Peter Gedeck; Klaus R Liedl
Journal:  J Med Chem       Date:  2014-04-16       Impact factor: 7.446

Review 9.  Target deconvolution techniques in modern phenotypic profiling.

Authors:  Jiyoun Lee; Matthew Bogyo
Journal:  Curr Opin Chem Biol       Date:  2013-01-18       Impact factor: 8.822

10.  PAINS in the assay: chemical mechanisms of assay interference and promiscuous enzymatic inhibition observed during a sulfhydryl-scavenging HTS.

Authors:  Jayme L Dahlin; J Willem M Nissink; Jessica M Strasser; Subhashree Francis; LeeAnn Higgins; Hui Zhou; Zhiguo Zhang; Michael A Walters
Journal:  J Med Chem       Date:  2015-02-21       Impact factor: 8.039

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  2 in total

1.  Computer-aided drug design at Boehringer Ingelheim.

Authors:  Ingo Muegge; Andreas Bergner; Jan M Kriegl
Journal:  J Comput Aided Mol Des       Date:  2016-09-20       Impact factor: 3.686

Review 2.  Applications of Deep-Learning in Exploiting Large-Scale and Heterogeneous Compound Data in Industrial Pharmaceutical Research.

Authors:  Laurianne David; Josep Arús-Pous; Johan Karlsson; Ola Engkvist; Esben Jannik Bjerrum; Thierry Kogej; Jan M Kriegl; Bernd Beck; Hongming Chen
Journal:  Front Pharmacol       Date:  2019-11-05       Impact factor: 5.810

  2 in total

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