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In silico de novo design of NNRTIs of HIV-1: Functional group based computational molecular modelling approach

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Title In silico de novo design of NNRTIs of HIV-1: Functional group based computational molecular modelling approach
 
Creator Raghuvanshi, U
Sapre, N S
 
Subject Back Propagation Neural Networks (BPNN)
De Novo Design
Molecular Modeling
Multiple Linear Regression (MLR)
NNRTIs
Support Vector Machine (SVM)
 
Description 1484-1493
Seven 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).
 
Date 2020-10-12T07:56:52Z
2020-10-12T07:56:52Z
2020-10
 
Type Article
 
Identifier 0975-0975(Online); 0376-4710(Print)
http://nopr.niscair.res.in/handle/123456789/55453
 
Language en_US
 
Rights CC Attribution-Noncommercial-No Derivative Works 2.5 India
 
Publisher NISCAIR-CSIR, India
 
Source IJC-A Vol.59A(10) [October 2020]