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The application of conformal prediction to the drug discovery process

  • Martin EklundEmail author
  • Ulf Norinder
  • Scott Boyer
  • Lars Carlsson
Article

Abstract

QSAR modeling is a method for predicting properties, e.g. the solubility or toxicity, of chemical compounds using machine learning techniques. QSAR is in widespread use within the pharmaceutical industry to prioritize compounds for experimental testing or to alert for potential toxicity during the drug discovery process. However, the confidence or reliability of predictions from a QSAR model are difficult to accurately assess. We frame the application of QSAR to preclinical drug development in an off-line inductive conformal prediction framework and apply it prospectively to historical data collected from four different assays within AstraZeneca over a time course of about five years. The results indicate weakened validity of the conformal predictor due to violations of the randomness assumption. The validity can be strengthen by adopting semi-off-line conformal prediction. The non-randomness of the data prevents exactly valid predictions but comparisons to the results of a traditional QSAR procedure applied to the same data indicate that conformal predictions are highly useful in the drug discovery process.

Keywords

QSAR Conformal prediction Drug discovery Temporal model updating 

Mathematics Subject Classifications (2010)

62-07 92-08 68T05 68U20 68U07 62H99 

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Copyright information

© Springer Science+Business Media Dordrecht 2013

Authors and Affiliations

  • Martin Eklund
    • 1
    • 2
    Email author
  • Ulf Norinder
    • 3
  • Scott Boyer
    • 2
  • Lars Carlsson
    • 2
  1. 1.Pharmaceutical BiosciencesUppsala UniversityUppsalaSweden
  2. 2.AstraZeneca Research and DevelopmentMölndalSweden
  3. 3.H. Lundbeck A/SValbyDenmark

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