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.
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