Please use this identifier to cite or link to this item: https://doi.org/10.2174/156802606778108942
Title: Application of support vector machines to in silico prediction of cytochrome P450 enzyme substrates and inhibitors
Authors: Yap, C.W. 
Xue, Y.
Li, Z.R.
Chen, Y.Z. 
Keywords: ADME
Adverse drug reaction
Compound
Computer aided drug design
CYP
Cytochrome P450
Drug
Enzyme
Inhibitor
Pharmacokinetics
Statistical learning methods
Substrate
Support vector machines
Toxicity
Issue Date: Aug-2006
Citation: Yap, C.W., Xue, Y., Li, Z.R., Chen, Y.Z. (2006-08). Application of support vector machines to in silico prediction of cytochrome P450 enzyme substrates and inhibitors. Current Topics in Medicinal Chemistry 6 (15) : 1593-1607. ScholarBank@NUS Repository. https://doi.org/10.2174/156802606778108942
Abstract: Cytochrome P450 enzymes are responsible for phase I metabolism of the majority of drugs and xenobiotics. Identification of the substrates and inhibitors of these enzymes is important for the analysis of drug metabolism, prediction of drug-drug interactions and drug toxicity, and the design of drugs that modulate cytochrome P450 mediated metabolism. The substrates and inhibitors of these enzymes are structurally diverse. It is thus desirable to explore methods capable of predicting compounds of diverse structures without over-fitting. Support vector machine is an attractive method with these qualities, which has been employed for predicting the substrates and inhibitors of several cytochrome P450 isoenzymes as well as compounds of various other pharmacodynamic, pharmacokinetic, and toxicological properties. This article introduces the methodology, evaluates the performance, and discusses the underlying difficulties and future prospects of the application of support vector machines to in silico prediction of cytochrome P450 substrates and inhibitors. © 2006 Bentham Science Publishers Ltd.
Source Title: Current Topics in Medicinal Chemistry
URI: http://scholarbank.nus.edu.sg/handle/10635/106614
ISSN: 15680266
DOI: 10.2174/156802606778108942
Appears in Collections:Staff Publications

Show full item record
Files in This Item:
There are no files associated with this item.

SCOPUSTM   
Citations

22
checked on May 19, 2018

WEB OF SCIENCETM
Citations

20
checked on Apr 3, 2018

Page view(s)

55
checked on May 18, 2018

Google ScholarTM

Check

Altmetric


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.