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Indexed by:期刊论文

Date of Publication:2012-06-01

Journal:INTERNATIONAL JOURNAL OF MOLECULAR SCIENCES

Included Journals:SCIE、PubMed、Scopus

Volume:13

Issue:6

Page Number:7015-7037

ISSN No.:1422-0067

Key Words:FBPase inhibitor; chemoinformatics methods; genetic algorithm; random forest

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 Mold(2) 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 r(ncv)(2) and cross-validated r(cv)(2) 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 r(pred)(2) of 0.90. More importantly, the building GA-RF model also passed through various criteria suggested by Tropsha and Roy with r(o)(2) and r(m)(2) 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.