1. Development of a New Predictive Model for Interactions with Human Cytochrome P450 2A6 Using Pharmacophore Ensemble/Support Vector Machine (PhE/SVM) Approach
- Author
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Yen-Ming Chen, Hong-Bin Chen, Po-Hong Chen, and Max K. Leong
- Subjects
Models, Molecular ,Protein Conformation ,Computer science ,In silico ,Pharmacology toxicology ,Pharmaceutical Science ,Computational biology ,Bioinformatics ,Substrate Specificity ,Cytochrome P-450 CYP2A6 ,Structure-Activity Relationship ,Pharmaceutical technology ,Humans ,Computer Simulation ,Pharmacology (medical) ,Enzyme Inhibitors ,Pharmacology ,Binding Sites ,Molecular Structure ,Drug discovery ,Organic Chemistry ,Reproducibility of Results ,Support vector machine ,Drug Design ,Computer-Aided Design ,Molecular Medicine ,Aryl Hydrocarbon Hydroxylases ,Pharmacophore ,Biotechnology ,Human cytochrome - Abstract
The objective of this investigation was to yield a generalized in silico model to quantitatively predict CYP2A6-substrates/inhibitors interactions to facilitate drug discovery.The newly invented pharmacophore ensemble/support vector machine (PhE/SVM) scheme was employed to generate the prediction model based on the data compiled from the literature.The predictions by the PhE/SVM model are in good agreement with the experimental observations for those molecules in the training set (n = 24, r (2) = 0.94, q (2) = 0.85, RMSE = 0.30) and the test set (n = 9, r (2) = 0.96, RMSE = 0.29). In addition, this in silico model performed equally well for those molecules in the external validation sets, namely one set of benzene and naphthalene derivatives (n = 45, r (2) = 0.81, RMSE = 0.46) and one set of amine neurotransmitters (n = 4, r (2) = 0.98, RMSE = 0.32). Furthermore, when compared with crystal structures, the calculated results are consistent with the published CYP2A6-substrate co-complex structure and the plasticity nature of CYP2A6 is also revealed.This PhE/SVM model is an accurate and robust model and can be utilized for predicting interactions with CYP2A6, high-throughput screening and data mining to facilitate drug discovery.
- Published
- 2008
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