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

Jiazhong Li, Shuyan Li, Beilei Lei, Huanxiang Liu, Xiaojun Yao, Mancang Liu, Paola Gramatica

Research output: Contribution to journalArticlepeer-review

7 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)973-985
Number of pages13
JournalJournal of Computational Chemistry
Volume31
Issue number5
DOIs
Publication statusPublished - 15 Apr 2010
Externally publishedYes

Keywords

  • Local lazy regression
  • Melanin-concentrating hormone receptor
  • Multiple linear regression

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