QSAR study of malonyl-CoA decarboxylase inhibitors using GA-MLR and a new strategy of consensus modeling

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

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48 Citations (Scopus)


Quantitative structure-activity relationship (QSAR) of a series of structural diverse malonyl-CoA decarboxylase (MCD) inhibitors have been investigated by using the predictive single model as well as the consensus analysis based on a new strategy proposed by us. Self-organizing map (SOM) neural network was employed to divide the whole data set into representative training set and test set. Then a multiple linear regressions (MLR) model population was built based on the theoretical molecular descriptors selected by Genetic Algorithm using the training set. In order to analyze the diversity of these models, multidimensional scaling (MDS) was employed to explore the model space based on the Hamming distance matrix calculated from each two models. In this space, Q2 (cross-validated R2) guided model selection (QGMS) strategy was performed to select submodels. Then consensus modeling was built by two strategies, average consensus model (ACM) and weighted consensus model (WCM), where each submodel had a different weight according to the contribution of model expressed by MLR regression coefficients. The obtained results prove that QGMS is a reliable and practical method to guide the submodel selection in consensus modeling building and our weighted consensus model (WCM) strategy is superior to the simple ACM.

Original languageEnglish
Pages (from-to)2636-2647
Number of pages12
JournalJournal of Computational Chemistry
Issue number16
Publication statusPublished - Dec 2008
Externally publishedYes


  • Average consensus model
  • Genetic algorithm
  • Malonyl coenzyme a decarboxylase
  • Q guided model selection
  • Sell-organizing map
  • Weighted consensus model


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