Chronic hepatitis C virus (HCV) infections are a significant health problem worldwide. The NS5B Polymerase
of HCV plays a central role in virus replication and is a prime target for the discovery of new treatment options. The
urgent need to develop novel anti-HCV agents has provided an impetus for understanding the structure-activity
relationship of novel Hepatitis C virus (HCV) NS5B polymerase inhibitors. Towards this objective, multiple linear
regression (MLR) and support vector machine (SVM) were used to develop quantitative structure-activity relationship
(QSAR) models for a dataset of 34 Tetrahydrobenzothiophene derivatives. The statistical analysis showed that the models
derived from both SVM (R2 = 0.9784, SE=0.2982, R2
cv = 0.92) and MLR (R2=0.9684, SE=0.1171, R2
cv= 0.955) have a
good internal predictivity. The models were also validated using external test set validation and Y-scrambling, the results
demonstrated that MLR has a significant predictive ability for the external dataset as compared to SVM. Also the model is
found to yield reliable clues for further optimization of Tetrahydrobenzothiophene derivatives in the data set.
Keywords: Hepatitis C virus, NS5B polymerase inhibitors, QSAR, support vector machine, tetrahydrobenzothiophene.
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