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231223s2010 xx |||||o 00| ||eng c |
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|a 10.1002/jcc.21383
|2 doi
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|a pubmed24n0635.xml
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|a (DE-627)NLM190594969
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|a (NLM)19670228
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|a DE-627
|b ger
|c DE-627
|e rakwb
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|a eng
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| 100 |
1 |
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|a Li, Jiazhong
|e verfasserin
|4 aut
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| 245 |
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|a 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
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|c 2010
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|a Text
|b txt
|2 rdacontent
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|a ƒaComputermedien
|b c
|2 rdamedia
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| 338 |
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|a ƒa Online-Ressource
|b cr
|2 rdacarrier
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| 500 |
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|a Date Completed 02.06.2010
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|a Date Revised 17.02.2010
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|a published: Print
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|a Citation Status MEDLINE
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|a 2009 Wiley Periodicals, Inc.
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|a 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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4 |
|a Journal Article
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|a Research Support, Non-U.S. Gov't
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| 650 |
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7 |
|a Pyrimidinones
|2 NLM
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| 650 |
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7 |
|a Receptors, Pituitary Hormone
|2 NLM
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| 650 |
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|a melanin-concentrating hormone receptor
|2 NLM
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| 700 |
1 |
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|a Li, Shuyan
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Lei, Beilei
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Liu, Huanxiang
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Yao, Xiaojun
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Liu, Mancang
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Gramatica, Paola
|e verfasserin
|4 aut
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| 773 |
0 |
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|i Enthalten in
|t Journal of computational chemistry
|d 1984
|g 31(2010), 5 vom: 15. Apr., Seite 973-85
|w (DE-627)NLM098138448
|x 1096-987X
|7 nnns
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| 773 |
1 |
8 |
|g volume:31
|g year:2010
|g number:5
|g day:15
|g month:04
|g pages:973-85
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| 856 |
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|u http://dx.doi.org/10.1002/jcc.21383
|3 Volltext
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|d 31
|j 2010
|e 5
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|h 973-85
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