Prediction of acetylcholinesterase inhibitors and characterization of correlative molecular descriptors by machine learning methods.
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% approximately 88.0% for AChEIs and 74.3% approximately 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.
Lv
Wei
W
Key Laboratory of Green Chemistry and Technology in Ministry of Education, College of Chemistry, Sichuan University, Chengdu 610064, PR China.
Xue
Ying
Y
D000465:Algorithms; D001185:Artificial Intelligence; D002800:Cholinesterase Inhibitors / Q000737:chemistry*; D019295:Computational Biology / Q000379:methods; D015195:Drug Design; D008956:Models, Chemical; D021281:Quantitative Structure-Activity Relationship
D002800:Cholinesterase Inhibitors