Journal of Computer-Aided Molecular Design

, Volume 24, Issue 12, pp 957–960 | Cite as

Making priors a priority

  • Matthew Segall
  • Andrew Chadwick


When we build a predictive model of a drug property we rigorously assess its predictive accuracy, but we are rarely able to address the most important question, “How useful will the model be in making a decision in a practical context?” To answer this requires an understanding of the prior probability distribution (“the prior”) and hence prevalence of negative outcomes due to the property being assessed. In this perspective, we illustrate the importance of the prior to assess the utility of a model in different contexts: to select or eliminate compounds, to prioritise compounds for further investigation using more expensive screens, or to combine models for different properties to select compounds with a balance of properties. In all three contexts, a better understanding of the prior probabilities of adverse events due to key factors will improve our ability to make good decisions in drug discovery, finding higher quality molecules more efficiently.


Prior Availability bias Decision tree Multiparameter optimization 


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Copyright information

© Springer Science+Business Media B.V. 2010

Authors and Affiliations

  1. 1.Optibrium LtdCambridgeUK
  2. 2.Tessella plcStanhope Bretby, Burton upon Trent, StaffsUK

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