Classification of estrogen receptor-beta ligands on the basis of their binding affinities using support vector machine and linear discriminant analysis.

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Luan F, Liu HT, Ma WP, Fan BT

Classification of estrogen receptor-beta ligands on the basis of their binding affinities using support vector machine and linear discriminant analysis.

Eur J Med Chem. 2008 Jan;43(1):43-52. Epub 2007 Mar 18.

PubMed ID
17459530 [ View in PubMed
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Abstract

Classification models of estrogen receptor-beta ligands were proposed using linear and nonlinear models. The data set was divided into active and inactive classes on the basis of their binding affinities. The two-class problem (active, inactive) was firstly explored by linear classifier approach, linear discriminant analysis (LDA). In order to get a more accurate prediction model, the nonlinear novel machine learning technique, support vectors machine (SVM), was subsequently used to investigate. The heuristic method (HM) was used to pre-select the whole descriptor sets. The model containing eight descriptors founded by SVM, showed better predictive ability than LDA. The accuracy in prediction for the training, test and overall data sets are 92.9%, 85.8% and 91.4% for SVM, 83.1%, 76.1% and 81.9% for LDA, respectively. The results indicate that SVM can be used as a powerful modeling tool for QSAR studies.

DrugBank Data that Cites this Article

Binding Properties
DrugTargetPropertyMeasurementpHTemperature (°C)
2-(5-HYDROXY-NAPHTHALEN-1-YL)-1,3-BENZOOXAZOL-6-OLEstrogen receptor betaIC 50 (nM)5N/AN/ADetails
3-(6-HYDROXY-NAPHTHALEN-2-YL)-BENZO[D]ISOOXAZOL-6-OLEstrogen receptor betaIC 50 (nM)1.4N/AN/ADetails
PrinaberelEstrogen receptor betaIC 50 (nM)5N/AN/ADetails