Journal
MOLECULAR DIVERSITY
Volume 17, Issue 1, Pages 85-96Publisher
SPRINGER
DOI: 10.1007/s11030-012-9404-z
Keywords
Acyl-coenzyme A: cholesterol acyltransferase (ACAT) inhibitors; Classification models; Kohonen's self-organizing map (SOM); Support vector machine (SVM); Extended connectivity fingerprints (ECFP_4)
Categories
Funding
- National Natural Science Foundation of China [20605003, 20975011]
- Chemical Grid Project of Beijing University of Chemical Technology
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Using a self-organizing map (SOM) and support vector machine, two classification models were built to predict whether a compound is a selective inhibitor toward the two Acyl-coenzyme A: cholesterol acyltransferase (ACAT) isozymes, ACAT-1 and ACAT-2. A dataset of 97 ACAT inhibitors was collected. For each molecule, the global descriptors, 2D and 3D property autocorrelation descriptors and autocorrelation of surface properties were calculated from the program ADRIANA.Code. The prediction accuracies of the models (based on the training/ test set splitting by SOM method) for the test sets are 88.9 % for SOM1, 92.6 % for SVM1 model. In addition, the extended connectivity fingerprints (ECFP_4) for all the molecules were calculated and the structure-activity relationship of selective ACAT inhibitors was summarized, which may help find important structural features of inhibitors relating to the selectivity of ACAT isozymes.
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