title: 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.
abstract: 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.
authors: State Key Laboratory of Applied Organic Chemistry, Department of Chemistry, Lanzhou University, Lanzhou 730000, China.
authors: Jiazhong
authors: J
authors: Li
authors: Shuyan
authors: S
authors: Li
authors: Beilei
authors: B
authors: Lei
authors: Huanxiang
authors: H
authors: Liu
authors: Xiaojun
authors: X
authors: Yao
authors: Mancang
authors: M
authors: Liu
authors: Paola
authors: P
authors: Gramatica
mesh_terms: D006801:Humans; D015233:Models, Statistical; D011744:Pyrimidinones / Q000737:chemistry / Q000494:pharmacology*; D021281:Quantitative Structure-Activity Relationship; D011979:Receptors, Pituitary Hormone / Q000037:antagonists & inhibitors* / Q000378:metabolism
chemical_list: D011744:Pyrimidinones; D011979:Receptors, Pituitary Hormone; C092274:melanin-concentrating hormone receptor