{'fraction_non_alphanumeric': 0.0340632603406326, 'fraction_numerical': 0.0036496350364963502, 'mean_word_length': 5.37984496124031, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Quantitative Structure-activity Relationship of Acetylcholinesterase Inhibitors based on mRMR Combined with Support Vector Regression

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.