HCVpred: A web server for predicting the bioactivity of hepatitis C virus NS5B inhibitors. Issue 20 (25th May 2020)

• Record Type: Journal Article
• Authors: Malik, Aijaz Ahmad; Phanus‐umporn, Chuleeporn; Schaduangrat, Nalini; Shoombuatong, Watshara; Isarankura‐Na‐Ayudhya, Chartchalerm; Nantasenamat, Chanin
• Is Part Of: Journal of computational chemistry. Volume 41:Issue 20(2020)
• Page Start: 1820
• Page End: 1834
• Publication Date: 2020-05-25
• Subjects: data science -- flavivirus -- HCVpred -- hepatitis C virus -- NS5B -- QSAR -- structure–activity relationship -- web server; Chemistry -- Data processing -- Periodicals; 542.85
• DOI: 10.1002/jcc.26223
• Languages: English
• ISSNs: 0192-8651

Abstract

Abstract: Hepatitis C virus (HCV) is one of the major causes of liver disease affecting an estimated 170 million people culminating in 300, 000 deaths from cirrhosis or liver cancer. NS5B is one of three potential therapeutic targets against HCV (i.e., the other two being NS3/4A and NS5A) that is central to viral replication. In this study, we developed a classification structure–activity relationship (CSAR) model for identifying substructures giving rise to anti‐HCV activities among a set of 578 non‐redundant compounds. NS5B inhibitors were described by a set of 12 fingerprint descriptors and predictive models were constructed from 100 independent data splits using the random forest algorithm. The modelability (MODI index) of the data set was determined to be robust with a value of 0.88 exceeding established threshold of 0.65. The predictive performance was deduced by the accuracy, sensitivity, specificity, and Matthews correlation coefficient, which was found to be statistically robust (i.e., the former three parameters afforded values in excess of 0.8 while the latter statistical parameter provided a value >0.7). An in‐depth analysis of the top 20 important descriptors revealed that aromatic ring and alkyl side chains are important for NS5B inhibition. Finally, the predictive model is deployed as a publicly accessible HCVpred web server (available at http://codes.bio/hcvpred/ ) that would allow users to predict the biological activity as being active or inactive against HCV NS5B. Thus, the knowledge and web server presented herein can be used in the design of more potent and specific drugs against the HCV NS5B. Abstract : Classification structure–activity relationship study was performed on a non‐redundant set of compounds whereby compounds are classified as active or inactive with respect to NS5B inhibitory potential. Model interpretation revealed the importance of aromatic ring and alkyl side chains for NS5B inhibition. Finally, the model is deployed as the HCVpred web server so as to facilitate in silico prediction of NS5B inhibitory potential.