An integrated ligand-based modelling approach to explore the structure-property relationships of influenza endonuclease inhibitors

Abstract Influenza endonuclease plays important role in the viral transcription and translation processes. Inhibition of endonuclease enzyme may be an interesting choice to restrict influenza infection. This current study deals with validated multi-chemometric modelling approaches namely regression-based and classification-based quantitative structure-activity relationships (QSARs), hologram QSAR, comparative molecular similarity analysis (CoMSIA), Open3DQSAR study and pharmacophore mapping to identify the structural and physicochemical requirements along with the chemico-biological interactions of pyridinones and pyranones for anti-endonuclease activity. The results suggest that the pyridinone scaffold is more preferable than the pyranone ring. The keto function at 4th position and aryl tetrazole substitution at 1st position of the parent moiety may be important for endonuclease inhibition. Hydroxyl group at 5th position of the parent ring may act as hydrogen bond acceptor feature. The steric substituent is suitable at 2nd position whereas hydrophobic substitution is found to be unfavourable at this position. Bulky hydrophobic substituents are not preferred at the 3rd position of the parent moiety. The information revealed from these integrated ligand-based modelling methods may provide useful informations for designing newer potential anti-influenza agents in future..

Medienart:

E-Artikel

Erscheinungsjahr:

2017

Erschienen:

2017

Enthalten in:

Zur Gesamtaufnahme - volume:28

Enthalten in:

Structural chemistry - 28(2017), 6 vom: 15. März, Seite 1663-1678

Sprache:

Englisch

Beteiligte Personen:

Amin, Sk. Abdul [VerfasserIn]
Adhikari, Nilanjan [VerfasserIn]
Gayen, Shovanlal [VerfasserIn]
Jha, Tarun [VerfasserIn]

Links:

Volltext [lizenzpflichtig]

BKL:

35.00$jChemie: Allgemeines

Themen:

Bayesian classification modelling
CoMSIA
HQSAR
Influenza endonuclease inhibitors
Open3DQSAR
Pharmacophore mapping

Anmerkungen:

© Springer Science+Business Media New York 2017

doi:

10.1007/s11224-017-0933-z

funding:

Förderinstitution / Projekttitel:

PPN (Katalog-ID):

OLC2083765559