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Vol. 7, No. 2, May 2022

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QSAR Study on Aromatic Disulfide Compounds as SARS-CoV Mpro Inhibitor Using Genetic Algorithm-Support Vector Machine

https://doi.org/10.22219/kinetik.v7i2.1428
Rizki Amanullah Hakim
Telkom University
Annisa Aditsania
Telkom University
Isman Kurniawan
Telkom University

Corresponding Author(s) : Rizki Amanullah Hakim

rizkiamanullah723@gmail.com

Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control, Vol. 7, No. 2, May 2022

Abstract

COVID-19 is a type of pneumonia caused by the Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2). This virus causes severe acute respiratory syndrome and 2 million active cases of COVID-19 have been found worldwide. A new strain of the SARS-CoV-2 virus emerged that proved to be more virulent than its predecessor. Regarding the design of a new inhibitor for this strain, SARS-CoV Main Protease (Mpro) was used as the target inhibitor. In the in silico development, the Quantitative Structure-Activity Relationship (QSAR) method is commonly used to predict the biological activity of unknown compounds to improve the process of drug design of a disease, including COVID-19. In this study, we aim to develop a QSAR model to predict the activity of aromatic disulfide compounds as SARS-CoV Mpro inhibitors using Genetic Algorithm (GA) – Support Vector Machine (SVM). GA was used for feature selection, while SVM was used for model prediction. The used dataset is set of features of aromatic disulfide compounds, along with information on the toxicity activity. We found that the best SVM model was obtained through the implementation of the polynomial kernel with the value of R2­­train and R2test­ scores are 0.952 and 0.676, respectively.

Keywords

Main Protease (Mpro) Quantitative-structure Activity Relationship (QSAR) Genetic Algorithm Support Vector Machine
Hakim, R. A., Aditsania, A. ., & Kurniawan, I. (2022). QSAR Study on Aromatic Disulfide Compounds as SARS-CoV Mpro Inhibitor Using Genetic Algorithm-Support Vector Machine. Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control, 7(2). https://doi.org/10.22219/kinetik.v7i2.1428
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References
  1. C. Drosten, W. Preiser, S. Günther, H. Schmitz, and H. W. Doerr, “severe acute respiratory syndrome: Identification of the etiological agent,” Trends Mol. Med., vol. 9, no. 8, pp. 325–327, 2003, doi https://doi.org/10.1016/s1471-4914(03)00133-3
  2. A. Remuzzi and G. Remuzzi, “COVID-19 and Italy: what next?” Lancet, vol. 395, no. 10231, pp. 1225–1228, 2020, doi: https://doi.org/10.1016/S0140-6736(20)30627-9
  3. H. Yang et al., “Design of wide-spectrum inhibitors targeting coronavirus main proteases,” PLoS Biol., vol. 3, no. 10, 2005, doi: https://doi.org/10.1371/journal.pbio.0030324
  4. W. W. C. Topley and S. G. S. Wilson, “Topley and Wilson’s Microbiology and Microbial Infections, 8 Volume Set, 10th Edition,” J. Infect., vol. 38, no. 2, p. 3500, 1999, [Online]. Available: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC501099/pdf/jclinpath00276-0077e.pdf
  5. Worldometers, “No Title,” 2020. https://www.worldometers.info/coronavirus/ (accessed Apr. 02, 2021). https://www.worldometers.info/coronavirus/
  6. S. A. Amin, S. Bhargava, N. Adhikari, S. Gayen, and T. Jha, “Exploring pyrazolo[3,4-d]pyrimidine phosphodiesterase 1 (PDE1) inhibitors: a predictive approach combining comparative validated multiple molecular modelling techniques,” J. Biomol. Struct. Dyn., vol. 36, no. 3, pp. 590–608, 2018, doi: https://doi.org/10.1080/07391102.2017.1288659
  7. S. Jain, S. A. Amin, N. Adhikari, T. Jha, and S. Gayen, “Good and bad molecular fingerprints for human rhinovirus 3C protease inhibition: identification, validation, and application in designing of new inhibitors through Monte Carlo-based QSAR study,” J. Biomol. Struct. Dyn., vol. 38, no. 1, pp. 66–77, 2020, doi: https://doi.org/10.1080/07391102.2019.1566093
  8. Y. Yang et al., “The deadly coronaviruses: The 2003 SARS pandemic and the 2020 novel coronavirus epidemic in China,” J. Autoimmun., vol. 109, no. February, p. 102434, 2020, doi: https://doi.org/10.1016/j.jaut.2020.102434
  9. L. Wang et al., “Discovery of unsymmetrical aromatic disulfides as novel inhibitors of SARS-CoV main protease: Chemical synthesis, biological evaluation, molecular docking and 3D-QSAR study,” Eur. J. Med. Chem., vol. 137, pp. 450–461, 2017, doi: https://doi.org/10.1016/j.ejmech.2017.05.045
  10. A. Golbraikh and A. Tropsha, “Predictive QSAR modeling based on diversity sampling of experimental datasets for the training and test set selection,” J. Comput. Aided. Mol. Des., vol. 16, no. 5–6, pp. 357–369, 2002, doi: https://doi.org/10.1023/a:1020869118689
  11. D. B. Kitchen, H. Decornez, J. R. Furr, and J. Bajorath, “Docking and scoring in virtual screening for drug discovery: Methods and applications,” Nat. Rev. Drug Discov., vol. 3, no. 11, pp. 935–949, 2004, doi: https://doi.org/10.1038/nrd1549
  12. J. M. Halperin et al., “Training Executive, Attention, and Motor Skills: A Proof-of-Concept Study in Preschool Children With ADHD,” J. Atten. Disord., vol. 17, no. 8, pp. 711–721, 2013, doi:. https://doi.org/10.1177/1087054711435681
  13. A. A. Toropov, A. P. Toropova, A. M. Veselinović, D. Leszczynska, and J. Leszczynski, “SARS-CoV Mpro inhibitory activity of aromatic disulfide compounds: QSAR model,” J. Biomol. Struct. Dyn., pp. 1–7, 2020, doi:. https://dx.doi.org/10.1080%2F07391102.2020.1818627
  14. L. Wang et al., “Discovery of unsymmetrical aromatic disulfides as novel inhibitors of SARS-CoV main protease: Chemical synthesis, biological evaluation, molecular docking and 3D-QSAR study,” Eur. J. Med. Chem., vol. 137, pp. 450–461, 2017, doi: https://doi.org/10.1016/j.ejmech.2017.05.045
  15. A. Gholamy, V. Kreinovich, and O. Kosheleva, “Why 70 / 30 or 80 / 20 Relation Between Training and Testing Sets : A Pedagogical Explanation,” pp. 1–6. https://www.cs.utep.edu/vladik/2018/tr18-09.pdf
  16. S. Chtita et al., “QSAR study of unsymmetrical aromatic disulfides as potent avian SARS-CoV main protease inhibitors using quantum chemical descriptors and statistical methods,” Chemom. Intell. Lab. Syst., vol. 210, no. February 2021, doi: https://doi.org/10.1016/j.chemolab.2021.104266
  17. E. Pourbasheer, R. Aalizadeh, and M. R. Ganjali, “QSAR study of CK2 inhibitors by GA-MLR and GA-SVM methods,” Arab. J. Chem., vol. 12, no. 8, pp. 2141–2149, 2019, doi: http://dx.doi.org/10.1016/j.arabjc.2014.12.021
  18. E. Pourbasheer, S. Vahdani, D. Malekzadeh, R. Aalizadeh, and A. Ebadi, “Qsar study of 17β-HSD3 inhibitors by genetic algorithm-support vector machine as a target receptor for the treatment of prostate cancer,” Iranian Journal of Pharmaceutical Research, vol. 16, no. 3. pp. 966–980, 2017, doi: https://doi.org/10.22037/ijpr.2017.2096
  19. E. Pourbasheer, S. Riahi, M. R. Ganjali, and P. Norouzi, “Application of genetic algorithm-support vector machine (GA-SVM) for prediction of BK- channels activity,” Eur. J. Med. Chem., vol. 44, no. 12, pp. 5023–5028, 2009, doi: https://doi.org/10.1016/j.ejmech.2009.09.006
  20. M. H. Fatemi and S. Gharaghani, “A novel QSAR model for prediction of apoptosis-inducing activity of 4-aryl-4-H-chromenes based on support vector machine,” Bioorganic Med. Chem., vol. 15, no. 24, pp. 7746–7754, 2007, doi: https://doi.org/10.1016/j.bmc.2007.08.057
  21. R. Burbidge, M. Trotter, B. Buxton, and S. Holden, “Drug design by machine learning: Support vector machines for pharmaceutical data analysis,” Comput. Chem., vol. 26, no. 1, pp. 5–14, 2001, doi: https://doi.org/10.1016/S0097-8485(01)00094-8
  22. S. Abe, “Support Vector Machines for Pattern Classification My Research History on NN , FS , and SVM,” Sci. Technol. Doi : http://dx.doi.org/10.1109/TCYB.2013.2279167
  23. F. Liu, C. Cao, and B. Cheng, “A quantitative structure-property relationship (QSPR) Study Of aliphatic alcohols by the method of dividing the molecular structure into substructure,” Int. J. Mol. Sci., vol. 12, no. 4, pp. 2448–2462, 2011, doi: https://doi.org/10.3390/ijms12042448
  24. H. F. Azmi, K. M. Lhaksmana, and I. Kurniawan, “QSAR Study of Fusidic Acid Derivative as Anti-Malaria Agents by using Artificial Neural Network-Genetic Algorithm,” 2020 8th Int. Conf. Inf. Commun. Technol. ICoICT 2020, pp. 3–6, 2020, doi: https://doi.org/10.1109/ICoICT49345.2020.9166158
  25. F. Rahman, K. M. Lhaksmana, and I. Kurniawan, “Implementation of Simulated Annealing-Support Vector Machine on QSAR Study of Fusidic Acid Derivatives as Anti-Malarial Agent,” 6th Int. Conf. Interact. Digit. Media, ICIDM 2020, no. Icidm, pp. 8–11, 2020, doi: https://doi.org/10.1109/ICIDM51048.2020.9339632
  26. I. Kurniawan, M. S. Fareza, and P. Iswanto, “Comfa, molecular docking and molecular dynamics studies on cycloguanil analogues as potent antimalarial agents,” Indones. J. Chem., vol. 21, no. 1, pp. 66–76, 2021, doi: https://doi.org/10.22146/ijc.52388
  27. Y. Yuliana, “Corona virus diseases (Covid-19): Sebuah tinjauan literatur,” Wellness Heal. Mag., vol. 2, no. 1, pp. 187–192, 2020, doi: https://doi.org/10.30604/well.95212020
  28. M. D. Christian Drosten, M.D., Stephan Günther, M.D., Wolfgang Preiser, M.D., Sylvie van der Werf, Ph.D., Hans-Reinhard Brodt, M.D., Stephan Becker, Ph.D., Holger Rabenau, Ph.D., Marcus Panning, M.D., Larissa Kolesnikova, Ph.D., Ron A.M. Fouchier, Ph.D., Annema, “Identification of a Novel Coronavirus in Patients with Severe Acute Respiratory Syndrome,” pp. 1967–1976, 2020. Doi : https://doi.org/10.1056/nejmoa030747
  29. PDPI, “Panduan Praktik Klinis: Pneumonia COVID-19,” J. Am. Pharm. Assoc., vol. 55, no. 5, pp. 1–67, 2020.
  30. J. Ivanov et al., “Quantitative structure−activity relationship machine learning models and their applications for identifying viral 3Clpro- And RDRP-targeting compounds as potential therapeutics for Covid-19 and related viral infections,” ACS Omega, vol. 5, no. 42, pp. 27344–27358, 2020, doi: https://doi.org/10.1021/acsomega.0c03682
  31. J. H. Holland, Adaption in natural and artificial systems. Michigan: The University of Michigan Press, Ann Arbor, MI, 1975. doi : https://dl.acm.org/doi/10.5555/531075
  32. C. HM, Applications of artificial intelligence in chemistry. Oxford: Oxford University Press, Oxford, 1993.
  33. M. F. Asshiddiqi, Perbandingan Metode Decision Tree dan Support Vector Machine untuk Analisis Sentimen pada Instagram Mengenai Kinerja PSSI. Universitas Telkom, 2020. Accessed: Nov. 30, 2020. [Online]. Available: /home/catalog/id/163055/slug/perbandingan-metode-decision-tree-dansupport-vector-machine-untuk-analisis-sentimen-pada-instagram-mengenai-kinerja-pssi.html
  34. I. Aydin, M. Karakose, and E. Akin, “A multi-objective artificial immune algorithm for parameter optimization in support vector machine,” Appl. Soft Comput. J., vol. 11, no. 1, pp. 120–129, 2011, doi: https://doi.org/10.1016/j.asoc.2009.11.003
  35. M. Ghifari, “Gif’s note Support Vector Machines: Penjelasan Matematis dan Intuitif,” pp. 1–13, 2021.
  36. Sahigara, F., Mansouri, K., Ballabio, D., Mauri, A., Consonni, V., and Todeschini, R., 2012, Comparison of different approaches to define the applicability domain of QSAR models, Molecules, 17 (5), 4791–4810. https://doi.org/10.3390/molecules17054791
  37. S. Yildirim, “Support Vector Machine - Explained,” 2020, [Online]. Available: https://towardsdatascience.com/support-vector-machine-explained-8d75fe8738fd
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