Simple jQuery Dropdowns
Please use this identifier to cite or link to this item:
Title: On the origins of Hepatitis C Virus NS5B polymerase inhibitory activity using machine learning approaches
Authors: Apilak Worachartcheewan
Veda Prachayasittikul
Nuttapat Anuwongcharoen
Watshara Shoombuatong
Virapong Prachayasittikul
Chanin Nantasenamat
Mahidol University
Keywords: Medicine
Issue Date: 1-Jul-2015
Citation: Current Topics in Medicinal Chemistry. Vol.15, No.18 (2015), 1814-1826
Abstract: © 2015 Bentham Science Publishers. Inhibition of non-structural protein 5B (NS5B) represents an attractive strategy for the therapeutic treatment of hepatitis C virus (HCV). In this study, machine learning classifiers such as artificial neural network (ANN), support vector machine (SVM), random forest (RF) and decision tree (DT) analyses were used to classify 970 compounds based on their physicochemical properties, in-cluding quantum chemical descriptors, constitutional descriptors, functional groups and molecular properties. Good predictive performance was obtained from all classifiers, providing accuracies ranging from 82.47–89.61% for external validation set. SVM was noted as the best classifier, indicated by its highest accuracy of 89.61%. The analyses were performed on data sets stratified by structural scaffolds (nucleoside and non-nucleoside) and bioactivities (active and inactive properties). In addition, a molecular fragment analysis was performed to investigate molecular substructures corresponding to biological activities. Furthermore, common substructures and potential functional groups governing the activities of active and inactive inhibitors were noted for the benefit of rational design and high-throughput screening towards potential HCV NS5B inhibitors.
ISSN: 18734294
Appears in Collections:Scopus 2011-2015

Files in This Item:
There are no files associated with this item.

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.