ArticleInternational journal of molecular sciences2025
Enhancing HCV NS3 Inhibitor Classification with Optimized Molecular Fingerprints Using Random Forest.
Article in International journal of molecular sciences, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- A novel approach for enhancing the potency of kinase inhibitors using topological water networks.Journal of cheminformatics · 2026Article
- MLGT: A multimodal graph attention network for virtual screening of anti-Uveitis drugs.PloS one · 2026Article
- Drug repurposing targeting COVID-19 3CL protease using molecular docking and machine learning regression approaches.Scientific reports · 2025Article
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1 author.
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Abstract
The classification of Hepatitis C virus (HCV) NS3 inhibitors is essential for identifying potential antiviral agents through computational methods. This study aims to develop an optimized machine learning (ML) model using random forest (RF) and molecular fingerprints to accurately classify HCV NS3 inhibitors. A dataset of 965 molecules was retrieved from the ChEMBL database, and 290 bioactive compounds were selected for model training. Twelve molecular fingerprint descriptors were tested, and the CDK graph-only fingerprint yielded the best performance. In addition to RF, performance comparisons of other classifiers such as instance-based k-nearest neighbor (IBk), logistic regression (LR), AdaBoost, and OneR were conducted using WEKA with various molecular fingerprint descriptors. The optimized RF model achieved an accuracy of 89.6552%, a mean absolute error (MAE) of 0.2114, a root mean square error (RMSE) of 0.3304, and a Matthews correlation coefficient (MCC) of 0.7950 on the test set. These results highlight the effectiveness of optimized molecular fingerprints in enhancing virtual screening (VS) for HCV inhibitors. This approach offers a data-driven method for drug discovery.
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