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IJSTR >> Volume 9 - Issue 6, June 2020 Edition



International Journal of Scientific & Technology Research  
International Journal of Scientific & Technology Research

Website: http://www.ijstr.org

ISSN 2277-8616



Machine Learning Model For Predicting Anti-Dengue Drugs: A Three-Dimensional Quantitative Structure–Activity Relationship (3D QSAR) Study

[Full Text]

 

AUTHOR(S)

Ali Qusay Khalid, Vasudeva Rao Avupati, Husniza Hussain

 

KEYWORDS

Drug Discovery, Machine Learning, Atom-Based 3D QSAR, Anti-Dengue Drugs, Combined Effects Contour Maps.

 

ABSTRACT

Over the last five decades, dengue virus (DENV) emerged epidemically in many countries of the world which particularly located in tropical and sub-tropical areas. The elevation of its incidence up to 30-fold causing 50-100 million of dengue fever (DF) cases across the global in more than 100 countries. Due to this vast prevalence almost half of the population in the tropical and sub-tropical regions in the world are at risk of causing infection. There exist very few studies reported in developing machine learning three-dimensional quantitative structure-activity relationship (3D QSAR) modelling on anti-dengue compounds. Hence, in this study, a series of substituted 1,3,4-oxadiazole derivatives with corresponding anti-dengue activities were considered as ligand data set to develop and validate Schrodinger Phase™ 3D QSAR model based on the atoms present in the molecules forming the dataset. Further, this model was exposed to elucidate the relationship between structural features and anti-dengue activities. The established 3D QSAR model is statistically significant (R2 Training Set = 0.73 Q2 (R2 Test Set) = 0.78) with good predictive power. In addition, combined effects contour maps (blue: positive potential & red: negative potential) of this model were critically analyzed and elucidated the pharmacophore features responsible for the observed anti-dengue activities. The pharmacophore model mapped in this study is used as a directive tool for virtual screening and to identify new in silico hits in anti-dengue drug discovery and development pipeline.

 

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