Publications and Presentations

Overcoming psychological barriers to good discovery decisions

Overcoming psychological barriers to good discovery decisions

A. T. Chadwick and M. D. Segall, Drug Discovery Today, 2010, 15 (13/14), pp. 561-569.
DOI: 10.1016/j.drudis.2010.05.007

This article explores the psychological barriers and risks of cognitive biases to R&D decision-making. It contrasts current practice with the use of evidence-based medicine by healthcare practitioners. It touches on how computational tools, such as StarDrop‘s integrated suite of discovery software, can help support good decision making in drug discovery.


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 may also contribute.

You can download a preprint of this article on psychological barriers in discovery decisions via the button below. Alternatively, visit the journal webpage to discover the final published version of this article. For more information on how to use computational tools to support good discovery decisions, explore our StarDrop webpages.


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With its comprehensive suite of integrated software, StarDrop™ delivers best-in-class in silico technologies within a highly visual and user-friendly interface. StarDrop™ enables a seamless flow from the latest data through predictive modelling to decision-making regarding the next round of synthesis and research, improving the speed, efficiency, and productivity of the drug optimisation and discovery process.