Publication:
Exploring the chemical space of influenza neuraminidase inhibitors

dc.contributor.authorNuttapat Anuwongcharoenen_US
dc.contributor.authorWatshara Shoombuatongen_US
dc.contributor.authorTanawut Tantimongcolwaten_US
dc.contributor.authorVirapong Prachayasittikulen_US
dc.contributor.authorChanin Nantasenamaten_US
dc.contributor.otherMahidol Universityen_US
dc.date.accessioned2018-12-11T02:03:00Z
dc.date.accessioned2019-03-14T08:03:06Z
dc.date.available2018-12-11T02:03:00Z
dc.date.available2019-03-14T08:03:06Z
dc.date.issued2016-01-01en_US
dc.description.abstract© 2016 Anuwongcharoen et al. The fight against the emergence of mutant influenza strains has led to the screening of an increasing number of compounds for inhibitory activity against influenza neuraminidase. This study explores the chemical space of neuraminidase inhibitors (NAIs), which provides an opportunity to obtain further molecular insights regarding the underlying basis of their bioactivity. In particular, a large set of 347 and 175 NAIs against influenza A and B, respectively, was compiled from the literature. Molecular and quantum chemical descriptors were obtained from low-energy conformational structures geometrically optimized at the PM6 level. The bioactivities of NAIs were classified as active or inactive according to their half maximum inhibitory concentration (IC50) value in which IC50< 1 μM and ≥ 10 μM were defined as active and inactive compounds, respectively. Interpretable decision rules were derived from a quantitative structure-activity relationship (QSAR) model established using a set of substructure descriptors via decision tree analysis. Univariate analysis, feature importance analysis from decision tree modeling and molecular scaffold analysis were performed on both data sets for discriminating important structural features amongst active and inactive NAIs. Good predictive performance was achieved as deduced from accuracy and Matthews correlation coefficient values in excess of 81% and 0.58, respectively, for both influenza A and B NAIs. Furthermore, molecular docking was employed to investigate the binding modes and their moiety preferences of active NAIs against both influenza A and B neuraminidases. Moreover, novel NAIs with robust binding fitness towards influenzaA and B neuraminidase were generated via combinatorial library enumeration and their binding fitness was on par or better than FDA-approved drugs. The results from this study are anticipated to be beneficial for guiding the rational drug design of novel NAIs for treating influenza infections.en_US
dc.identifier.citationPeerJ. Vol.2016, No.4 (2016)en_US
dc.identifier.doi10.7717/peerj.1958en_US
dc.identifier.issn21678359en_US
dc.identifier.other2-s2.0-84966312406en_US
dc.identifier.urihttps://repository.li.mahidol.ac.th/handle/20.500.14594/42081
dc.rightsMahidol Universityen_US
dc.rights.holderSCOPUSen_US
dc.source.urihttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=84966312406&origin=inwarden_US
dc.subjectAgricultural and Biological Sciencesen_US
dc.subjectBiochemistry, Genetics and Molecular Biologyen_US
dc.titleExploring the chemical space of influenza neuraminidase inhibitorsen_US
dc.typeArticleen_US
dspace.entity.typePublication
mu.datasource.scopushttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=84966312406&origin=inwarden_US

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