Quantitative Structure–activity Relationship Modeling of Some Naphthoquinone Derivatives as Inhibitors of Pathogenic Agent IDO1

Sajjad Jazayeri Farsani; Saeid Asadpour; Abolfazl Semnani; Shima Ghanavati Nasab

doi:10.4103/jrptps.JRPTPS_124_20

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https://doi.org/10.4103/jrptps.JRPTPS_124_20

Quantitative Structure–activity Relationship Modeling of Some Naphthoquinone Derivatives as Inhibitors of Pathogenic Agent IDO1

Author(s):

Sajjad Jazayeri Farsani¹,

Saeid Asadpour^1,*,

Abolfazl Semnani¹,

Shima Ghanavati Nasab¹

1Department of Chemistry, Faculty of Sciences, University of Shahrekord, Shahrekord, Iran

Journal of Reports in Pharmaceutical Sciences:Vol. 10, issue 2; 317-34

Published online:Dec 17, 2021

Article type:Research Article

Received:Oct 13, 2020

Accepted:Oct 03, 2021

How to Cite:Jazayeri Farsani S, Asadpour S, Semnani A, Ghanavati Nasab S. Quantitative Structure–activity Relationship Modeling of Some Naphthoquinone Derivatives as Inhibitors of Pathogenic Agent IDO1. J Rep Pharm Sci. 2021;10(2):e146972. doi: https://doi.org/10.4103/jrptps.JRPTPS_124_20

Abstract

Quantitative structure–activity relationship (QSAR) was performed to analyze naphthoquinone derivatives as an inhibitor of indoleamine 2,3-dioxygenase pathogen via multivariate regression (MLR) and artificial neural network. The best descriptors were picked to construct the QSAR. Two sets of exercises and experiments were also performed using Principal Component Analysis for multiple linear regression (MLR). A quantitative model was then proposed based on these analyses and the activity of the compounds based on multivariate statistical analysis was interpreted. The study finally revealed that although the MLR model can predict the activity of the compounds to some extent, the artificial neural network (ANN) model results indicate that the predictions obtained by the neural network are much better and more efficient than other models. The neural network was also used where three coefficients of correlation were used. The results uncovered that the ANN model is statistically significant and has good stability for data validation for the validation method. Share Descriptive relationships of structure and activity were also examined.

Keywords

Artificial neural network (ANN)

multiple linear regression (MLR)

naphthoquinone derivatives

pathogenic agent

quantitative structure–activity relationship (QSAR)

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Author(s):

Sajjad Jazayeri Farsani:[PubMed][Scholar]
Saeid Asadpour:[PubMed][Scholar]
Abolfazl Semnani:[PubMed][Scholar]
Shima Ghanavati Nasab:[PubMed][Scholar]

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Quantitative Structure–activity Relationship Modeling of Some Naphthoquinone Derivatives as Inhibitors of Pathogenic Agent IDO1

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