Screening Herbal Compound Candidates for Use as Anti-Inflammatory Drugs for COVID-19 Treatment Using Deep Semisupervised Learning
Keywords:
COVID-19, deep learning, drug repurposing, hyperinflammation, semisupervised learning
Abstract
COVID-19 is a disease caused by the SARS-CoV-2 virus. Its symptoms include cough, fever, shortness of breath, and acute inflammation (hyperinflammation), and severe cases can lead to death. These symptoms tend to worsen if inflammation is not controlled. This research aims to build a stacked autoencoder deep neural network (SAE-DNN) model for identifying herbal compound candidates that can be used as anti-inflammatory drugs for COVID-19 treatment. The model’s performance is evaluated on the basis of different data representations. The research process involves data collection, data preprocessing, modeling, and testing the model on the herbal data to obtain herbal compound candidates. Results indicate that the developed SAE-DNN model with compound representation that combines fingerprints and dipeptide composition produces the best performance with an accuracy of 0.96722, a recall of 0.96419, area under the receiver operating characteristic of 0.99596, and an F1 score of 0.96567. A total of 33 herbal compounds are found as candidate anti-inflammatory drugs by using the SAE-DNN model.
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Bahi, M., & Batouche, M. (2018). Deep semi-supervised learning for DTI prediction using large datasets and H2O-spark platform (p. 7). https://doi.org/10.1109/ISACV.2018.8354081
Fernández-de Gortari, E., García-Jacas, C. R., Martinez-Mayorga, K., & Medina- Franco, J. L. (2017). Database fingerprint (DFP): An approach to represent molecular databases. Journal of Cheminformatics, 9(1), 9. https://doi.org/10.1186/s13321-017-0195-1
Frazier, P. I. (2018). A Tutorial on Bayesian Optimization. ArXiv:1807.02811 [Cs, Math, Stat]. http://arxiv.org/abs/1807.02811
Gao, Y., Bielohuby, M., Fleming, T., Grabner, G. F., Foppen, E., Bernhard, W., Guzmán-Ruiz, M., Layritz, C., Legutko, B., Zinser, E., García-Cáceres, C., Buijs, R. M., Woods, S. C., Kalsbeek, A., Seeley, R. J., Nawroth, P. P., Bidlingmaier, M., Tschöp, M. H., & Yi, C.-X. (2017). Dietary sugars, not lipids, drive hypothalamic inflammation. Molecular Metabolism, 6(8), 897–908. https://doi.org/10.1016/j.molmet.2017.06.008
García, L. F. (2020). Immune Response, Inflammation, and the Clinical Spectrum of COVID-19. Frontiers in Immunology, 11, 1441. https://doi.org/10.3389/fimmu.2020.01441
Günther, S., Kuhn, M., Dunkel, M., Campillos, M., Senger, C., Petsalaki, E., Ahmed, J., Urdiales, E., Gewiess, A., Jensen, L., Schneider, R., Skoblo, R., Russell, R., Bourne, P., Bork, P., & Preissner, R. (2008). SuperTarget and Matador: Resources for exploring drug-target relationships. Nucleic Acids Research, 36, D919-22. https://doi.org/10.1093/nar/gkm862
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Kim, S., Chen, J., Cheng, T., Gindulyte, A., He, J., He, S., Li, Q., Shoemaker, B. A.,
Thiessen, P. A., Yu, B., Zaslavsky, L., Zhang, J., & Bolton, E. E. (2021). PubChem in 2021: New data content and improved web interfaces. Nucleic Acids Research, 49(D1), D1388–D1395. https://doi.org/10.1093/nar/gkaa971
Li, S., Li, S., Disoma, C., Zheng, R., Zhou, M., Razzaq, A., Liu, P., Zhou, Y., Dong, Z.,
Du, A., Peng, J., Hu, L., Huang, J., Feng, P., Jiang, T., & Xia, Z. (2020). SARS-
CoV-2: Mechanism of infection and emerging technologies for future prospects.
Reviews in Medical Virology, n/a(n/a), e2168. https://doi.org/10.1002/rmv.2168 Mulyani, H., & Widyastuti, S. H. (2016). Tumbuhan Herbal Sebagai Jamu Pengobatan Tradisional Terhadap Penyakit Dalam Serat Primbon Jampi Jawi Jilid I. Jurnal
Penelitian Humaniora, 21(2), 19.
Shereen, M. A., Khan, S., Kazmi, A., Bashir, N., & Siddique, R. (2020). COVID-19 infection: Origin, transmission, and characteristics of human coronaviruses. Journal of Advanced Research, 24, 91–98. https://doi.org/10.1016/j.jare.2020.03.005
Sulistiawan, F., Kusuma, W. A., Ramadhanti, N. S., & Tedjo, A. (2020). Drug-Target Interaction Prediction in Coronavirus Disease 2019 Case Using Deep Semi- Supervised Learning Model. 2020 International Conference on Advanced Computer Science and Information Systems (ICACSIS), 83–88. https://doi.org/10.1109/ICACSIS51025.2020.9263241
Syahdi, R. R., Iqbal, J. T., Munim, A., & Yanuar, A. (2019). HerbalDB 2.0: Optimization of Construction of Three-Dimensional Chemical Compound Structures to Update Indonesian Medicinal Plant Database. Pharmacognosy Journal, 11(6).
The UniProt Consortium. (2021). UniProt: The universal protein knowledgebase in 2021. Nucleic Acids Research, 49(D1), D480–D489. https://doi.org/10.1093/nar/gkaa1100
Widyatmoko, D. (2018). Upaya Pemanfaatan Biodiversitas Tumbuhan Dalam Rangka Konservasi yang Dapat Digunakan Sebagai Pembelajaran. Seminar Nasional Pendidikan Biologi dan Biologi 2018, 41–55.
Xiao, N., Cao, D.-S., Zhu, M.-F., & Xu, Q.-S. (2015). protr/ProtrWeb: R package and web server for generating various numerical representation schemes of protein sequences. Bioinformatics, 31(11), 1857–1859. https://doi.org/10.1093/bioinformatics/btv042
Xue, H., Li, J., Xie, H., & Wang, Y. (2018). Review of Drug Repositioning Approaches and Resources. International Journal of Biological Sciences, 14(10), 1232–1244. PubMed. https://doi.org/10.7150/ijbs.24612
Yiu, H. H., Graham, A. L., & Stengel, R. F. (2012). Dynamics of a cytokine storm. PloS One, 7(10), e45027. https://doi.org/10.1371/journal.pone.0045027
Yuan, H., Ma, Q., Ye, L., & Piao, G. (2016). The Traditional Medicine and Modern Medicine from Natural Products. Molecules (Basel, Switzerland), 21(5), 559. PubMed. https://doi.org/10.3390/molecules21050559
Zhou, Y., Wang, F., Tang, J., Nussinov, R., & Cheng, F. (2020). Artificial intelligence in COVID-19 drug repurposing. The Lancet Digital Health, 2(12), e667–e676. https://doi.org/10.1016/S2589-7500(20)30192-8
Published
2023-04-03
How to Cite
Khalid, I. A., Kusuma, W. A. K., Priandana, K., Batubara, I., & Heryanto, R. (2023). Screening Herbal Compound Candidates for Use as Anti-Inflammatory Drugs for COVID-19 Treatment Using Deep Semisupervised Learning. Indonesian Journal of Pharmacy, 34(2), 302–311. https://doi.org/10.22146/ijp.3629
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Research Article
