Pattern recognition approach to classifying CYP 2C19 isoform

Bartosz Krawczyk 1
  • 1 Department of Systems and Computer Networks, Wroclaw University of Technology, Wybrzeze Wyspianskiego 27, 50-370, Wroclaw, Poland

Abstract

In this paper a pattern recognition approach to classifying quantitative structure-property relationships (QSPR) of the CYP2C19 isoform is presented. QSPR is a correlative computer modelling of the properties of chemical molecules and is widely used in cheminformatics and the pharmaceutical industry. Predicting whether or not a particular chemical will be metabolized by 2C19 is of primary importance to the pharmaceutical industry. This task poses certain challenges. First of all analyzed data are characterized by a significant biological noise. Additionally the training set is unbalanced, with objects from negative class outnumbering the positives four times. Presented solution deals with those problems, additionally incorporating a throughout feature selection for improving the stability of received results. A strong emphasis is put on the outlier detection and proper model validation to achieve the best predictive power.

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