Abstract:
: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.
journal_name
J Comput Aided Mol Desjournal_title
Journal of computer-aided molecular designauthors
Segall M,Chadwick Adoi
10.1007/s10822-010-9388-7subject
Has Abstractpub_date
2010-12-01 00:00:00pages
957-60issue
12eissn
0920-654Xissn
1573-4951journal_volume
24pub_type
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