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• W1981197056 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." @default.
• W1981197056 created "2016-06-24" @default.
• W1981197056 creator A5042637290 @default.
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• W1981197056 date "2012-06-07" @default.
• W1981197056 modified "2023-10-14" @default.
• W1981197056 title "Toward the Prediction of FBPase Inhibitory Activity Using Chemoinformatic Methods" @default.
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• W1981197056 doi "https://doi.org/10.3390/ijms13067015" @default.
• W1981197056 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/3397509" @default.
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