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Research ArticleArticle

Classification of Cytochrome P450 1A2 Inhibitors and Noninhibitors by Machine Learning Techniques

Poongavanam Vasanthanathan, Olivier Taboureau, Chris Oostenbrink, Nico P. E. Vermeulen, Lars Olsen and Flemming Steen Jørgensen
Drug Metabolism and Disposition March 2009, 37 (3) 658-664; DOI: https://doi.org/10.1124/dmd.108.023507
Poongavanam Vasanthanathan
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Olivier Taboureau
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Chris Oostenbrink
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Nico P. E. Vermeulen
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Lars Olsen
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Flemming Steen Jørgensen
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Abstract

The cytochrome P450 (P450) superfamily plays an important role in the metabolism of drug compounds, and it is therefore highly desirable to have models that can predict whether a compound interacts with a specific isoform of the P450s. In this work, we provide in silico models for classification of CYP1A2 inhibitors and noninhibitors. Training and test sets consisted of approximately 400 and 7000 compounds, respectively. Various machine learning techniques, such as binary quantitative structure activity relationship, support vector machine (SVM), random forest, kappa nearest neighbor (kNN), and decision tree methods were used to develop in silico models, based on Volsurf and Molecular Operating Environment descriptors. The best models were obtained using the SVM, random forest, and kNN methods in combination with the BestFirst variable selection method, resulting in models with 73 to 76% of accuracy on the test set prediction (Matthews correlation coefficients of 0.51 and 0.52). Finally, a decision tree model based on Lipinski's Rule-of-Five descriptors was also developed. This model predicts 67% of the compounds correctly and gives a simple and interesting insight into the issue of classification. All of the models developed in this work are fast and precise enough to be applicable for virtual screening of CYP1A2 inhibitors or noninhibitors or can be used as simple filters in the drug discovery process.

Footnotes

  • The work was supported in part by the Drug Research Academy; the Nanoscience Biotechnology and IT program [Grant 26-04-0296]; the Danish Medical Research Council [Grant 271-07-0783]; and the Carlsberg Foundation [Grant 04-0290/20].

  • Article, publication date, and citation information can be found at http://dmd.aspetjournals.org.

  • doi:10.1124/dmd.108.023507.

  • ABBREVIATIONS: P450, cytochrome P450; SVM, support vector machine; kNN, kappa nearest neighbor; QSAR, quantitative structure activity relationship; 3D, three-dimensional; 2D, two-dimensional; MOE, molecular operating environment; DOOD, D-optimal onion design; PCA, principle component analysis; PC, principle component; MCC, Mathews correlation coefficient.

    • Received July 25, 2008.
    • Accepted December 1, 2008.
  • The American Society for Pharmacology and Experimental Therapeutics
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Drug Metabolism and Disposition: 37 (3)
Drug Metabolism and Disposition
Vol. 37, Issue 3
1 Mar 2009
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Research ArticleArticle

Classification of Cytochrome P450 1A2 Inhibitors and Noninhibitors by Machine Learning Techniques

Poongavanam Vasanthanathan, Olivier Taboureau, Chris Oostenbrink, Nico P. E. Vermeulen, Lars Olsen and Flemming Steen Jørgensen
Drug Metabolism and Disposition March 1, 2009, 37 (3) 658-664; DOI: https://doi.org/10.1124/dmd.108.023507

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Research ArticleArticle

Classification of Cytochrome P450 1A2 Inhibitors and Noninhibitors by Machine Learning Techniques

Poongavanam Vasanthanathan, Olivier Taboureau, Chris Oostenbrink, Nico P. E. Vermeulen, Lars Olsen and Flemming Steen Jørgensen
Drug Metabolism and Disposition March 1, 2009, 37 (3) 658-664; DOI: https://doi.org/10.1124/dmd.108.023507
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