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. View Full-Text
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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.