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Evaluation of predictive machine learning models for drug repurposing against delta variant of SARS-CoV-2 spike protein

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Title Evaluation of predictive machine learning models for drug repurposing against delta variant of SARS-CoV-2 spike protein
 
Creator Dash, Sudipta
Ishani, Ishani
Lahiri, Dibyajit
Nag, Moupriya
 
Subject Drug repurposing
Machine learning
Molecular docking
Regression model
SARS-CoV-2
 
Description 879-891
Drug repurposing is a major approach used by researchers to tackle the COVID-19 pandemic which has been worsened
by the current surge of delta variant in many countries. Though drugs like Remdesivir and Hydroxychloroquine have been
repurposed, studies prove these drugs have insignificant effect in treatment. So, in this study, we use the already FDA
approved database of 1615 drugs to apply semi-flexible and flexible molecular docking methods to calculate the docking
scores and identify the best 20 potential inhibitors for our modelled delta variant spike protein RBD. Then, we calculate
2325 1-D and 2-D molecular descriptors and use machine-learning algorithms like K-Nearest Neighbor, Random Forest,
Support Vector Machine and ensemble stacking method to build regression-based prediction models. We identify 15 best
descriptors for the dataset all of which were found to be inversely correlated with ligand binding. With only these few
descriptors, the models performed excellently with an area under curve (AUC) value of 0.952 in Regression Error
Characteristic curve for ensemble stacking. Therefore, we comment that these 15 descriptors are the most important features
for the binding of inhibitors to the spike protein and hence these should be studied properly in terms of drug repurposing and
drug discovery.
 
Date 2022-08-30T09:16:35Z
2022-08-30T09:16:35Z
2022-09
 
Type Article
 
Identifier 0975-0959 (Online); 0301-1208 (Print)
http://nopr.niscpr.res.in/handle/123456789/60406
https://doi.org/10.56042/ijbb.v59i9.56888
 
Language en
 
Publisher NIScPR-CSIR, India
 
Source IJBB Vol.59(9) [SEP 2022]