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Updated: Sep 4, 2025
Quantitative Structure-Activity Relationship, Activity Prediction, and Molecular Dynamics of Non-nucleotide Reverse Transcriptase Inhibitors
Published on: May 9, 2025
Targeting non-structural proteins of Hepatitis C virus for predicting repurposed drugs using QSAR and machine
Sakshi Kamboj1,2, Akanksha Rajput1, Amber Rastogi1,2
1Virology Unit and Bioinformatics Centre, Institute of Microbial Technology, Council of Scientific and Industrial Research (CSIR), Sector 39A, Chandigarh 160036, India.
Abstract:
Hepatitis C virus (HCV) infection causes viral hepatitis leading to hepatocellular carcinoma. Despite the clinical use of direct-acting antivirals (DAAs) still there is treatment failure in 5-10% cases. Therefore, it is crucial to develop new antivirals against HCV. In this endeavor, we developed the "Anti-HCV" platform using machine learning and quantitative structure-activity relationship (QSAR) approaches to predict repurposed drugs targeting HCV non-structural (NS) proteins. We retrieved experimentally validated small molecules from the ChEMBL database with bioactivity (IC50/EC50) against HCV NS3 (454), NS3/4A (495), NS5A (494) and NS5B (1671) proteins. These unique compounds were divided into training/testing and independent validation datasets. Relevant molecular descriptors and fingerprints were selected using a recursive feature elimination algorithm. Different machine learning techniques viz. support vector machine, k-nearest neighbour, artificial neural network, and random forest were used to develop the predictive models. We achieved Pearson's correlation coefficients from 0.80 to 0.92 during 10-fold cross validation and similar performance on independent datasets using the best developed models. The robustness and reliability of developed predictive models were also supported by applicability domain, chemical diversity and decoy datasets analyses. The "Anti-HCV" predictive models were used to identify potential repurposing drugs. Representative candidates were further validated by molecular docking which displayed high binding affinities. Hence, this study identified promising repurposed drugs viz. naftifine, butalbital (NS3), vinorelbine, epicriptine (NS3/4A), pipecuronium, trimethaphan (NS5A), olodaterol and vemurafenib (NS5B) etc. targeting HCV NS proteins. These potential repurposed drugs may prove useful in antiviral drug development against HCV.
Insights
Researchers developed the Anti-HCV platform using machine learning to identify repurposed drugs for Hepatitis C virus (HCV) infection, addressing treatment failures with existing antivirals.
Area of Science:
- Computational chemistry and drug discovery
- Virology and infectious diseases
- Machine learning in pharmacology
Background:
- Hepatitis C virus (HCV) infection can lead to liver cancer.
- Direct-acting antivirals (DAAs) have limitations, with 5-10% treatment failure rates.
- New antiviral strategies against HCV are essential.
Purpose of the Study:
- To develop a predictive platform, "Anti-HCV", for identifying repurposed drugs against HCV.
- To target HCV non-structural (NS) proteins using machine learning and QSAR.
- To accelerate the discovery of novel antiviral therapies for HCV.
Main Methods:
- Utilized machine learning and Quantitative Structure-Activity Relationship (QSAR) on ChEMBL database compounds.
- Selected molecular descriptors and fingerprints using recursive feature elimination.
- Developed and validated predictive models using SVM, k-NN, ANN, and Random Forest algorithms.
Main Results:
- Achieved high predictive accuracy (Pearson's correlation coefficients 0.80-0.92) in cross-validation and independent datasets.
- Validated model robustness through applicability domain, chemical diversity, and decoy analyses.
- Identified promising repurposed drug candidates including naftifine, butalbital, vinorelbine, and vemurafenib targeting HCV NS proteins.
Conclusions:
- The "Anti-HCV" platform effectively predicts repurposed drugs for HCV.
- Identified specific drugs targeting NS3, NS3/4A, NS5A, and NS5B proteins show potential for antiviral development.
- These findings offer new avenues for combating HCV treatment failures.
