ArticleJournal of computer-aided molecular design2021
StackHCV: a web-based integrative machine-learning framework for large-scale identification of hepatitis C virus NS5B inhibitors.
Article in Journal of computer-aided molecular design, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed.
- Integrated deep learning and multi-scale modeling for the discovery of pan-genotypic HCV NS5B polymerase inhibitors.Molecular diversity · 2026Article
- Stacked ensemble learning and in-silico profiling reveal dual DPP-IV and SGLT2 inhibitors from Moringa oleifera metabolites.Scientific reports · 2026Article
- Virtual screening, optimization design, and synthesis analysis of novel benzofuran derivatives as pan-genotypic HCV NS5B polymerase inhibitors using molecular modeling.BMC chemistry · 2025Article
- BGATT-GR: accurate identification of glucocorticoid receptor antagonists based on data augmentation combined with BiGRU-attention.Scientific reports · 2025Article
- Enhancing HCV NS3 Inhibitor Classification with Optimized Molecular Fingerprints Using Random Forest.International journal of molecular sciences · 2025Article
- First report on chemometrics-driven multilayered lead prioritization in addressing oxysterol-mediated overexpression of G protein-coupled receptor 183.Molecular diversity · 2024Article
- MetaCGRP is a high-precision meta-model for large-scale identification of CGRP inhibitors using multi-view information.Scientific reports · 2024Article
- Classification models for predicting the bioactivity of pan-TRK inhibitors and SAR analysis.Molecular diversity · 2024Article
- Significance of Artificial Intelligence in the Study of Virus-Host Cell Interactions.Biomolecules · 2024Review
- Distinct Characteristic Binding Modes of Benzofuran Core Inhibitors to Diverse Genotypes of Hepatitis C Virus NS5B Polymerase: A Molecular Simulation Study.International journal of molecular sciences · 2024Article
- StackER: a novel SMILES-based stacked approach for the accelerated and efficient discovery of ERα and ERβ antagonists.Scientific reports · 2023Article
- Article
- TIPred: a novel stacked ensemble approach for the accelerated discovery of tyrosinase inhibitory peptides.BMC bioinformatics · 2023Article
- DeepAR: a novel deep learning-based hybrid framework for the interpretable prediction of androgen receptor antagonists.Journal of cheminformatics · 2023Article
- TROLLOPE: A novel sequence-based stacked approach for the accelerated discovery of linear T-cell epitopes of hepatitis C virus.PloS one · 2023Article
- StackPR is a new computational approach for large-scale identification of progesterone receptor antagonists using the stacking strategy.Scientific reports · 2022Article
- SCORPION is a stacking-based ensemble learning framework for accurate prediction of phage virion proteins.Scientific reports · 2022Article
- Targeting non-structural proteins of Hepatitis C virus for predicting repurposed drugs using QSAR and machine learning approaches.Computational and structural biotechnology journal · 2022Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Fast and accurate identification of inhibitors with potency against HCV NS5B polymerase is currently a challenging task. As conventional experimental methods is the gold standard method for the design and development of new HCV inhibitors, they often require costly investment of time and resources. In this study, we develop a novel machine learning-based meta-predictor (termed StackHCV) for accurate and large-scale identification of HCV inhibitors. Unlike the existing method, which is based on single-feature-based approach, we first constructed a pool of various baseline models by employing a wide range of heterogeneous molecular fingerprints with five popular machine learning algorithms (k-nearest neighbor, multi-layer perceptron, partial least squares, random forest and support vectors machine). Secondly, we integrated these baseline models in order to develop the final meta-based model by means of the stacking strategy. Extensive benchmarking experiments showed that StackHCV achieved a more accurate and stable performance as compared to its constituent baseline models on the training dataset and also outperformed the existing predictor on the independent test dataset. To facilitate the high-throughput identification of HCV inhibitors, we built a web server that can be freely accessed at http://camt.pythonanywhere.com/StackHCV . It is expected that StackHCV could be a useful tool for fast and precise identification of potential drugs against HCV NS5B particularly for liver cancer therapy and other clinical applications.
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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 Socratic graph.