Making priors a priority.

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 Des

authors

Segall M,Chadwick A

doi

10.1007/s10822-010-9388-7

subject

Has Abstract

pub_date

2010-12-01 00:00:00

pages

957-60

issue

12

eissn

0920-654X

issn

1573-4951

journal_volume

24

pub_type

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