A new strategy to improve the predictive ability of the local lazy regression and its application to the QSAR study of melanin-concentrating hormone receptor 1 antagonists
2009 Wiley Periodicals, Inc.
| Veröffentlicht in: | Journal of computational chemistry. - 1984. - 31(2010), 5 vom: 15. Apr., Seite 973-85 |
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| 1. Verfasser: | |
| Weitere Verfasser: | , , , , , |
| Format: | Online-Aufsatz |
| Sprache: | English |
| Veröffentlicht: |
2010
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| Zugriff auf das übergeordnete Werk: | Journal of computational chemistry |
| Schlagworte: | Journal Article Research Support, Non-U.S. Gov't Pyrimidinones Receptors, Pituitary Hormone melanin-concentrating hormone receptor |
| Zusammenfassung: | 2009 Wiley Periodicals, Inc. In the quantitative structure-activity relationship (QSAR) study, local lazy regression (LLR) can predict the activity of a query molecule by using the information of its local neighborhood without need to produce QSAR models a priori. When a prediction is required for a query compound, a set of local models including different number of nearest neighbors are identified. The leave-one-out cross-validation (LOO-CV) procedure is usually used to assess the prediction ability of each model, and the model giving the lowest LOO-CV error or highest LOO-CV correlation coefficient is chosen as the best model. However, it has been proved that the good statistical value from LOO cross-validation appears to be the necessary, but not the sufficient condition for the model to have a high predictive power. In this work, a new strategy is proposed to improve the predictive ability of LLR models and to access the accuracy of a query prediction. The bandwidth of k neighbor value for LLR is optimized by considering the predictive ability of local models using an external validation set. This approach was applied to the QSAR study of a series of thienopyrimidinone antagonists of melanin-concentrating hormone receptor 1. The obtained results from the new strategy shows evident improvement compared with the commonly used LOO-CV LLR methods and the traditional global linear model |
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| Beschreibung: | Date Completed 02.06.2010 Date Revised 17.02.2010 published: Print Citation Status MEDLINE |
| ISSN: | 1096-987X |
| DOI: | 10.1002/jcc.21383 |