Literature DB >> 20553956

Overcoming psychological barriers to good discovery decisions.

Andrew T Chadwick1, Matthew D Segall.   

Abstract

Better individual and team decision-making should enhance R&D performance. Reproducible biases affecting human decision-making, known as cognitive biases, are well understood by psychologists. These threaten objectivity and balance and so are credible causes for continuing unpleasant surprises in Development and high operating costs. For four of the most common and insidious cognitive biases, we consider the risks to R&D decision-making and contrast current practice with use of evidence-based medicine by healthcare practitioners. Feedback on problem-solving performance in simulated environments could be one of the simplest ways to help teams improve their selection of compounds and effective screening sequences. Computational tools that encourage objective consideration of all of the available information might also contribute. 2010 Elsevier Ltd. All rights reserved.

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Year:  2010        PMID: 20553956     DOI: 10.1016/j.drudis.2010.05.007

Source DB:  PubMed          Journal:  Drug Discov Today        ISSN: 1359-6446            Impact factor:   7.851


  7 in total

1.  Can we really do computer-aided drug design?

Authors:  Matthew Segall
Journal:  J Comput Aided Mol Des       Date:  2011-12-11       Impact factor: 3.686

2.  Making priors a priority.

Authors:  Matthew Segall; Andrew Chadwick
Journal:  J Comput Aided Mol Des       Date:  2010-10-16       Impact factor: 3.686

3.  ALOHA: a novel probability fusion approach for scoring multi-parameter drug-likeness during the lead optimization stage of drug discovery.

Authors:  Derek A Debe; Ravindra B Mamidipaka; Robert J Gregg; James T Metz; Rishi R Gupta; Steven W Muchmore
Journal:  J Comput Aided Mol Des       Date:  2013-10-11       Impact factor: 3.686

4.  Guiding effective decisions: an interview with Matthew Segall, CEO of Optibrium. Interview by Wendy A. Warr.

Authors:  Matthew Segall
Journal:  J Comput Aided Mol Des       Date:  2011-01-12       Impact factor: 3.686

5.  The challenges of making decisions using uncertain data.

Authors:  Matthew D Segall; Edmund J Champness
Journal:  J Comput Aided Mol Des       Date:  2015-07-01       Impact factor: 3.686

6.  Mode of Action Analyses of Neferine, a Bisbenzylisoquinoline Alkaloid of Lotus (Nelumbo nucifera) against Multidrug-Resistant Tumor Cells.

Authors:  Onat Kadioglu; Betty Y K Law; Simon W F Mok; Su-Wei Xu; Thomas Efferth; Vincent K W Wong
Journal:  Front Pharmacol       Date:  2017-05-05       Impact factor: 5.810

Review 7.  Improving target assessment in biomedical research: the GOT-IT recommendations.

Authors:  Christoph H Emmerich; Lorena Martinez Gamboa; Martine C J Hofmann; Marc Bonin-Andresen; Olga Arbach; Pascal Schendel; Björn Gerlach; Katja Hempel; Anton Bespalov; Ulrich Dirnagl; Michael J Parnham
Journal:  Nat Rev Drug Discov       Date:  2020-11-16       Impact factor: 112.288

  7 in total

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