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Quantitative Structure-activity Relationship of Acetylcholinesterase Inhibitors based on mRMR Combined with Support Vector Regression

Author(s):

Jiaxiang Wu, Bowen Deng, Jeong Younseo, Yu Lan, Dongshu Du and Fuxue Chen*   Pages 1 - 7 ( 7 )

Abstract:


In this work, support vector regression (SVR), an effective machine learning method, proposed by Vapnik was applied to establish QSAR model for a series of AchEI. Fourteen descriptors were selected for constructing the SVR mode by using mRMR-Forward feature selection method. The parameters (ε, C) were adjusted by leave-one-out cross validation (LOOCV) method which was used to judge the predictive power of different models. After optimization, one optimal SVR-QSAR model was attained, and the mean relative errors (MRE) of LOOCV by using SVR is 1.72%. As a result, LogP negatively affected the activity, Refractivity and Water Accessible Surface Area positively affected the activity.

Keywords:

AD, AChEI, QSAR, SVM, mRMR, molecule descriptors

Affiliation:

Shanghai Key Laboratory of Bio-Crops, College of Life Science, Shanghai University, Shanghai, Shanghai Key Laboratory of Bio-Crops, College of Life Science, Shanghai University, Shanghai, Center for Bioinformatics and Computational Biology,Pai Chai University, Daejeon, Guangxi Medical University, Neurology department, Shanghai Key Laboratory of Bio-Crops, College of Life Science, Shanghai University, Shanghai, Shanghai Key Laboratory of Bio-Crops, College of Life Science, Shanghai University, Shanghai



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