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StackHCV: a web-based integrative machine-learning framework for large-scale identification of hepatitis C virus NS5B inhibitors

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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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Data availability

All the data are available at http://camt.pythonanywhere.com/StackHCV.

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Acknowledgements

This work was fully supported by College of Arts, Media and Technology, Chiang Mai University and partially supported by Chiang Mai University, Chiang Mai University High Performance Computer Service and Mahidol University. GOLD license was supported by Kasetsart University Research and Development Institute, KURDI (Grant No. FF (KU) 11.64).

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  1. Aijaz Ahmad Malik and Warot Chotpatiwetchkul have contributed equally to this work.

Authors and Affiliations

  1. Center of Data Mining and Biomedical Informatics, Faculty of Medical Technology, Mahidol University, Bangkok, 10700, Thailand

    Aijaz Ahmad Malik, Chuleeporn Phanus-umporn, Chanin Nantasenamat & Watshara Shoombuatong

  2. Applied Computational Chemistry Research Unit, Department of Chemistry, School of Science, King Mongkut’s Institute of Technology Ladkrabang, Bangkok, 10520, Thailand

    Warot Chotpatiwetchkul

  3. Modern Management and Information Technology, College of Arts, Media and Technology, Chiang Mai University, Chiang Mai, 50200, Thailand

    Phasit Charoenkwan

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  1. Aijaz Ahmad Malik
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Malik, A.A., Chotpatiwetchkul, W., Phanus-umporn, C. et al. StackHCV: a web-based integrative machine-learning framework for large-scale identification of hepatitis C virus NS5B inhibitors. J Comput Aided Mol Des 35, 1037–1053 (2021). https://doi.org/10.1007/s10822-021-00418-1

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