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Int. J. Mol. Sci. 2012, 13(6), 7015-7037; doi:10.3390/ijms13067015

Toward the Prediction of FBPase Inhibitory Activity Using Chemoinformatic Methods

Department of Materials Science and Chemical Engineering, Dalian University of Technology, Dalian 116023, Liaoning, China
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Author to whom correspondence should be addressed.
Received: 23 April 2012 / Revised: 18 May 2012 / Accepted: 31 May 2012 / Published: 7 June 2012

Abstract

Currently, Chemoinformatic methods are used to perform the prediction for FBPase inhibitory activity. A genetic algorithm-random forest coupled method (GA-RF) was proposed to predict fructose 1,6-bisphosphatase (FBPase) inhibitors to treat type 2 diabetes mellitus using the Mold2 molecular descriptors. A data set of 126 oxazole and thiazole analogs was used to derive the GA-RF model, yielding the significant non-cross-validated correlation coefficient r2ncv and cross-validated r2cv values of 0.96 and 0.67 for the training set, respectively. The statistically significant model was validated by a test set of 64 compounds, producing the prediction correlation coefficient r2pred of 0.90. More importantly, the building GA-RF model also passed through various criteria suggested by Tropsha and Roy with r2o and r2m values of 0.90 and 0.83, respectively. In order to compare with the GA-RF model, a pure RF model developed based on the full descriptors was performed as well for the same data set. The resulting GA-RF model with significantly internal and external prediction capacities is beneficial to the prediction of potential oxazole and thiazole series of FBPase inhibitors prior to chemical synthesis in drug discovery programs.
Keywords: FBPase inhibitor; chemoinformatics methods; genetic algorithm; random forest FBPase inhibitor; chemoinformatics methods; genetic algorithm; random forest
This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

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MDPI and ACS Style

Hao, M.; Zhang, S.; Qiu, J. Toward the Prediction of FBPase Inhibitory Activity Using Chemoinformatic Methods. Int. J. Mol. Sci. 2012, 13, 7015-7037.

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