Please use this identifier to cite or link to this item: http://nopr.niscair.res.in/handle/123456789/55453
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dc.contributor.authorRaghuvanshi, U-
dc.contributor.authorSapre, N S-
dc.date.accessioned2020-10-12T07:56:52Z-
dc.date.available2020-10-12T07:56:52Z-
dc.date.issued2020-10-
dc.identifier.issn0975-0975(Online); 0376-4710(Print)-
dc.identifier.urihttp://nopr.niscair.res.in/handle/123456789/55453-
dc.description1484-1493en_US
dc.description.abstractSeven novel lead compounds, acting as NNRTIs of HIV-1, are extracted from a database of, in silico de novo designed, 500 compounds. Functional group based computational molecular modelling techniques are used for such design of Acylthiocarbamate derivatives. Effect of structural characteristics on the antiviral activity of these derivatives has also been studied. Statistical regression techniques namely, Non-linear (Back Propagation Neural Network, Support Vector Machine) and linear (Multiple Linear) chemometric regression methods are used in developing the relationships of Kier-Hall Electrotopological State Indices (ERingA, EO8, EN9, EO14, ES16, EN17, EO19, ER, and ER1) with the HIV-1 antiviral activity. The relative potentials of these methods are also assessed and the results suggest that BPNN (r2 = 0.845, MSE = 0.142, q2 = 0.818) describes the relationship between the descriptors and antiviral activity in a relatively better manner than SVM-ε-radial (r2 = 0.844, MSE = 0.144, q2 = 0.807) and MLR (r2 = 0.836, MSE = 0.150, q2 = 0.805).en_US
dc.language.isoen_USen_US
dc.publisherNISCAIR-CSIR, Indiaen_US
dc.rights CC Attribution-Noncommercial-No Derivative Works 2.5 Indiaen_US
dc.sourceIJC-A Vol.59A(10) [October 2020]en_US
dc.subjectBack Propagation Neural Networks (BPNN)en_US
dc.subjectDe Novo Designen_US
dc.subjectMolecular Modelingen_US
dc.subjectMultiple Linear Regression (MLR)en_US
dc.subjectNNRTIsen_US
dc.subjectSupport Vector Machine (SVM)en_US
dc.titleIn silico de novo design of NNRTIs of HIV-1: Functional group based computational molecular modelling approachen_US
dc.typeArticleen_US
Appears in Collections:IJC-A Vol.59A(10) [October 2020]

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