ArticleMolecular diversity2015
Large-scale structure-activity relationship study of hepatitis C virus NS5B polymerase inhibition using SMILES-based descriptors.
Article in Molecular diversity, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
4 citing papers in PubMed, 18 citations in OpenAlex.
- Machine learning-based prediction of SARS-CoV-2 bioactivity: integrating ICBMC bioinformatics · 2026Article
- Analytical and machine learning approaches identify a sea star steroid with promising activity for COVID-19 therapeutic development.Scientific reports · 2025Article
- Mpropred: A machine learning (ML) driven Web-App for bioactivity prediction of SARS-CoV-2 main protease (Mpro) antagonists.PloS one · 2023Article
- StackHCV: a web-based integrative machine-learning framework for large-scale identification of hepatitis C virus NS5B inhibitors.Journal of computer-aided molecular design · 2021Article
Corrections and comments
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Authors and funding
5 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Hepatitis C virus (HCV) is composed of structural and non-structural proteins involved in viral transcription and propagation. In particular, NS5B is an RNA-dependent RNA polymerase for viral transcription and genome replication and is a target for designing anti-viral agents. In this study, classification and quantitative structure-activity relationship (QSAR) models of HCV NS5B inhibitors were constructed using the Correlation and Logic software. Molecular descriptors for a set of 970 HCV NS5B inhibitors were encoded using the simplified molecular input line entry system notation, and predictive models were built via the Monte Carlo method. The QSAR models provided acceptable correlation coefficients of [Formula: see text] and [Formula: see text] in the ranges of 0.6038-0.7344 and 0.6171-0.7294, respectively, while the classification models displayed sensitivity, specificity, and accuracy in ranges of 88.24-98.84, 83.87-93.94, and 86.50-94.41 %, respectively. Furthermore, molecular fragments as substructures involved in increased and decreased inhibitory activities were explored. The results provide information on QSAR and classification models for high-throughput screening and mechanistic insights into the inhibitory activity of HCV NS5B polymerase.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.