Prediction of acetylcholinesterase inhibitors and characterization of correlative molecular descriptors by machine learning methods
Prediction of acetylcholinesterase inhibitors and characterization of correlative molecular descriptors by machine learning methods
复制标题
通过机器学习方法预测乙酰胆碱酯酶抑制剂并表征相关分子描述符
DOI：
10.1016/j.ejmech.2009.12.038
复制
发表时间：
2010-03-01
影响因子：
6.7
通讯作者：
Xue, Ying
中科院分区：
文献类型：
--
作者：
Lv, Wei;Xue, Ying
乙酰胆碱酯酶（AChE）已成为一个重要的药物靶点，其抑制剂已被证明在阿尔茨海默病的对症治疗中是有用的。本工作探讨了几种机器学习方法（支持向量机（SVM），k-最近邻（k-NN），和C4.5决策树（C4.5 DT））预测乙酰胆碱酯酶抑制剂（AChEI）。特征选择方法用于提高预测准确度和选择负责区分AChEI和非AChEI的分子描述符。基于三种机器学习方法的预测准确率分别为76.3%和88.0%，74.3%和79.6%。这项工作表明，机器学习方法，如支持向量机有利于预测AChEIs的潜在未知的化合物集，并展示与AChEIs相关的分子描述符。(C)2009年Elsevier Masson SAS。All rights reserved.
Acetylcholinesterase (AChE) has become an important drug target and its inhibitors have proved useful in the symptomatic treatment of Alzheimer's disease. This work explores several machine learning methods (support vector machine (SVM), k-nearest neighbor (k-NN), and C4.5 decision tree (C4.5 DT)) for predicting AChE inhibitors (AChEIs). A feature selection method is used for improving prediction accuracy and selecting molecular descriptors responsible for distinguishing AChEIs and non-AChEIs. The prediction accuracies are 76.3% similar to 88.0% for AChEIs and 74.3% similar to 79.6% for non-AChEIs based on the three kinds of machine learning methods. This work suggests that machine learning methods such as SVM are facilitating for predicting AChEIs potential of unknown sets of compounds and for exhibiting the molecular descriptors associated with AChEIs. (C) 2009 Elsevier Masson SAS. All rights reserved.