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.
Keywords: ADME, adverse drug reaction, compound, computer aided drug design, CYP, cytochrome P450, drug, enzyme, inhibitor, pharmacokinetics
Current Topics in Medicinal Chemistry
Title: Application of Support Vector Machines to In Silico Prediction of Cytochrome P450 Enzyme Substrates and Inhibitors
Volume: 6 Issue: 15
Author(s): C. W. Yap, Y. Xue and Y. Z. Chen
Affiliation:
Keywords: ADME, adverse drug reaction, compound, computer aided drug design, CYP, cytochrome P450, drug, enzyme, inhibitor, pharmacokinetics
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.
Export Options
About this article
Cite this article as:
Yap W. C., Xue Y. and Chen Z. Y., Application of Support Vector Machines to In Silico Prediction of Cytochrome P450 Enzyme Substrates and Inhibitors, Current Topics in Medicinal Chemistry 2006; 6 (15) . https://dx.doi.org/10.2174/156802606778108942
DOI https://dx.doi.org/10.2174/156802606778108942 |
Print ISSN 1568-0266 |
Publisher Name Bentham Science Publisher |
Online ISSN 1873-4294 |
Call for Papers in Thematic Issues
Chemistry Based on Natural Products for Therapeutic Purposes
The development of new pharmaceuticals for a wide range of medical conditions has long relied on the identification of promising natural products (NPs). There are over sixty percent of cancer, infectious illness, and CNS disease medications that include an NP pharmacophore, according to the Food and Drug Administration. Since NP ...read more
Current Trends in Drug Discovery Based on Artificial Intelligence and Computer-Aided Drug Design
Drug development discovery has faced several challenges over the years. In fact, the evolution of classical approaches to modern methods using computational methods, or Computer-Aided Drug Design (CADD), has shown promising and essential results in any drug discovery campaign. Among these methods, molecular docking is one of the most notable ...read more
Drug Discovery in the Age of Artificial Intelligence
In the age of artificial intelligence (AI), we have witnessed a significant boom in AI techniques for drug discovery. AI techniques are increasingly integrated and accelerating the drug discovery process. These developments have not only attracted the attention of academia and industry but also raised important questions regarding the selection ...read more
From Biodiversity to Chemical Diversity: Focus of Flavonoids
Flavonoids are the largest group of polyphenols, plant secondary metabolites arising from the essential aromatic amino acid phenylalanine (or more rarely from tyrosine) via the phenylpropanoid pathway. The flavan nucleus is the basic 15-carbon skeleton of flavonoids (C6-C3-C6), which consists of two phenyl rings (A and B) and a heterocyclic ...read more
- Author Guidelines
- Graphical Abstracts
- Fabricating and Stating False Information
- Research Misconduct
- Post Publication Discussions and Corrections
- Publishing Ethics and Rectitude
- Increase Visibility of Your Article
- Archiving Policies
- Peer Review Workflow
- Order Your Article Before Print
- Promote Your Article
- Manuscript Transfer Facility
- Editorial Policies
- Allegations from Whistleblowers
- Announcements