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%∼88.0% for AChEIs and 74.3%∼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.

Several machine learning methods such as support vector machine, k-nearest neighbor, and C4.5 decision tree were used to predict acetylcholinesterase inhibitors (AChEIs) and non- AChEIs.Figure optionsDownload as PowerPoint slide