448
Views
20
CrossRef citations to date
0
Altmetric
Special Issue: 9th International Symposium on Computational Methods in Toxicology and Pharmacology Integrating Internet Resources (CMTPI-2017) - Part 1. Guest Editors: A.K. Saxena and M. Saxena

In silico prediction of multiple-category classification model for cytochrome P450 inhibitors and non-inhibitors using machine-learning methodFootnote$

, , , &
Pages 863-874 | Received 27 Oct 2017, Accepted 29 Oct 2017, Published online: 29 Nov 2017

References

  • A.E. Klon, Machine learning algorithms for the prediction of hERG and CYP450 binding in drug development, Expert Opin. Drug Metab. Toxicol. 6 (2010), pp. 821–833.
  • M. Lapins, A. Worachartcheewan, O. Spjuth, V. Georgiev, V. Prachayasittikul, C. Nantasenamat, and J.E. Wikberg, A unified proteochemometric model for prediction of inhibition of cytochrome p450 isoforms, PLoS One 8 (2013), p. e66566.
  • R. Arimoto, Computational models for predicting interactions with cytochrome p450 enzyme, Curr. Top. Med. Chem. 6 (2006), pp. 1609–1618.
  • K. Roy and P.P. Roy, QSAR of cytochrome inhibitors, Expert Opin. Drug Metab. Toxicol. 5 (2009), pp. 1245–1266.
  • R.J. Vaz and R. Klabunde (ed.), Antitargets, Wiliey-VCH Verlag GmbH, Weinham, 2008.
  • R. Arimoto, Computational models for predicting interactions with cytochrome p450 enzyme, Curr. Topics Med. Chem. 6 (2006), pp. 1609–1618.
  • Y. Wang, J. Xiao, T.O. Suzek, J. Zhang, J. Wang, Z. Zhou, L. Han, K. Karapetyan, S. Dracheva, B.A. Shoemaker, E. Bolton, A. Gindulyte, and S.H. Bryant, PubChem’s bioAssay database, Nucleic Acids Res. 40 (2012), pp. D400–D412.
  • P. Vasanthanathan, O. Taboureau, C. Oostenbrink, N.P. Vermeulen, L. Olsen, and F.S. Jorgensen, Classification of cytochrome P450 1A2 inhibitors and noninhibitors by machine learning techniques, Drug Metab. Dispos. 37 (2009), pp. 658–664.
  • S. Novotarskyi, I. Sushko, R. Korner, A.K. Pandey, and I.V. Tetko, A comparison of different QSAR approaches to modeling CYP450 1A2 inhibition, J. Chem. Inf. Model. 51 (2011), pp. 1271–1280.
  • F. Cheng, Y. Yu, J. Shen, L. Yang, W. Li, G. Liu, P.W. Lee, and Y. Tang, Classification of cytochrome P450 inhibitors and noninhibitors using combined classifiers, J. Chem. Inf. Model. 51 (2011), pp. 996–1011.
  • H. Sun, H. Veith, M. Xia, C.P. Austin, and R. Huang, Predictive models for cytochrome p450 isozymes based on quantitative high throughput screening data, J. Chem. Inf. Model. 51 (2011), pp. 2474–2481.
  • M. Rostkowski, O. Spjuth, and P. Rydberg, WhichCyp: Prediction of cytochromes P450 inhibition, Bioinformatics 29 (2013), pp. 2051–2052.
  • B. Manavalan and J. Lee, SVMQA: Support-vector-machine-based protein single-model quality assessment, Bioinformatics 33 (2017), pp. 2496–2503.
  • B. Manavalan and J. Lee, Random forest-based protein model quality assessment (RFMQA) using structural features and potential energy terms, PLoS One 9 (2014), p. e106542.
  • B. Manavalan, S. Basith, T.H. Shin, S. Choi, M.O. Kim, and G. Lee, MLACP: Machine-learning-based prediction of anticancer peptides, Oncotarget 8 (2017), pp. 77121–77136.
  • J.H. Lee, S. Lee, and S. Choi, In silico classification of adenosine receptor antagonists using Laplacian-modified naive Bayesian, support vector machine, and recursive partitioning, J. Mol. Graph. Model. 28 (2010), pp. 883–890.
  • H. Veith, N. Southall, R. Huang, T. James, D. Fayne, N. Artemenko, M. Shen, J. Inglese, C.P. Austin, D.G. Lloyd, and D.S. Auld, Comprehensive characterization of cytochrome P450 isozyme selectivity across chemical libraries, Nat. Biotechnol. 27 (2009), pp. 1050–1055.
  • G. Cruciani, M. Pastor, and W. Guba, VolSurf: A new tool for the pharmacokinetic optimization of lead compounds, Eur. J. Pharm. Sci. 11 (2000), pp. S29–S39.
  • X.Y. Xia, E.G. Maliski, P. Gallant, and D. Rogers, Classification of kinase inhibitors using a Bayesian model, J. Med. Chem. 47 (2004), pp. 4463–4470.
  • P. Baldi, S. Brunak, Y. Chauvin, C.A.F. Andersen, and H. Nielsen, Assessing the accuracy of prediction algorithms for classification: An overview, Bioinformatics 16 (2000), pp. 412–424.
  • K.K. Chohan, S.W. Paine, J. Mistry, P. Barton, and A.M. Davis, A rapid computational filter for cytochrome P450 1A2 inhibition potential of compound libraries, J. Med. Chem. 48 (2005), pp. 5154–5161.
  • P. Vasanthanathan, O. Taboureau, C. Oostenbrink, N.P.E. Vermeulen, L. Olsen, and F.S. Jorgensen, Classification of cytochrome P450 1A2 inhibitors and noninhibitors by machine learning techniques, Drug Metab. Disp. 37 (2009), pp. 658–664.
  • S. Ekins, M.J. De Groot, and J.P. Jones, Pharmacophore and three-dimensional quantitative structure activity relationship methods for modeling cytochrome P450 active sites, Drug Metab. Disp. 29 (2001), pp. 936–944.

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.