Toward the Prediction of FBPase Inhibitory Activity Using Chemoinformatic Methods
AbstractCurrently, 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.
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Hao, M.; Zhang, S.; Qiu, J. Toward the Prediction of FBPase Inhibitory Activity Using Chemoinformatic Methods. Int. J. Mol. Sci. 2012, 13, 7015-7037.
Hao M, Zhang S, Qiu J. Toward the Prediction of FBPase Inhibitory Activity Using Chemoinformatic Methods. International Journal of Molecular Sciences. 2012; 13(6):7015-7037.Chicago/Turabian Style
Hao, Ming; Zhang, Shuwei; Qiu, Jieshan. 2012. "Toward the Prediction of FBPase Inhibitory Activity Using Chemoinformatic Methods." Int. J. Mol. Sci. 13, no. 6: 7015-7037.