Skip to main content

Advertisement

Classification of CYP450 1A2 inhibitors using PubChem data

Article metrics

  • 1191 Accesses

  • 2 Citations

Cytochromes P450 (CYP450) are a superfamily of enzymes, involved in metabolism of a large number of xenobiotic compounds. CYP450 are involved in degradation of a large amount of drugs, currently present on the market. The promiscuity with respect to substrates makes the CYP450 enzymes prone to inhibition by a large amount of drugs, which gives way to clinically significant drug-drug interactions.

In this work different machine learning methods were applied to classify the inhibitors/noninhibitors of human CYP450 1A2. The structures and the active/inactive classification concerning CYP1A2 inhibition were taken from PubChem BioAssay database. This assay uses human CYP1A2 to measure the demethylation of luciferin 6' methyl ether (Luciferin-ME; Promega-Glo) to luciferin.

The tested methods include k nearest neighbors (kNN), decision tree, random forest, support vector machine (SVM) and associative neural networks (ASNN). The descriptors used were those from the Dragon software, the fragment descriptors and the E-state indices.

The training and test sets were handled separately to avoid different possibilities of overfitting - including overfitting by descriptor selection. Different applicability domain (AD) approaches were used to estimate the confidence of classification.

As a result the models managed to correctly classify 80% of the test set instances. The accuracy of classification was found to be up to 95%, if only 30% most confident predictions were taken into account. The model was also applied to an external test set of 187 molecules, collected from literature and measured using a different etalon reaction. For this set accuracy of 78% was achieved on the 30% most confident predictions.

All the developed models are fast enough to be used for virtual screening of CYP1A2 inhibitors and noninhibitors. The developed models are publicly available on-line at the http://qspr.eu web site.

Author information

Correspondence to Sergii Novotarskyi.

Rights and permissions

Reprints and Permissions

About this article

Cite this article

Novotarskyi, S., Sushko, I., Körner, R. et al. Classification of CYP450 1A2 inhibitors using PubChem data. J Cheminform 2, P40 (2010) doi:10.1186/1758-2946-2-S1-P40

Download citation

Keywords

  • Support Vector Machine
  • Random Forest
  • Virtual Screening
  • Methyl Ether
  • Luciferin