QSAR Study for Prediction of HIV-1 Protease Inhibitor Using the Gravitational Search Algorithm–Neural Network (GSA-NN) Methods
Abstract
Human immunodeficiency virus (HIV) is a virus that infects an immune cell and makes the patient more susceptible to infections and other diseases. HIV is also a factor that leads to acquired immune deficiency syndrome (AIDS) disease. The active target that is usually used in the treatment of HIV is HIV-1 protease. Combining HIV-1 protease inhibitors and reverse-transcriptase inhibitors in highly active antiretroviral therapy (HAART) is typically used to treat this virus. However, this treatment can only reduce the viral load, restore some parts of the immune system, and failed to overcome the drug resistance. This study aimed to build a QSAR model for predicting HIV-1 protease inhibitor activity using the gravitational search algorithm-neural network (GSA-NN) method. The GSA method is used to select molecular descriptors, while NN was used to develop the prediction model. The improvement of model performance was found after performing the hyperparameter tuning procedure. The validation results show that model 3, containing seven descriptors, shows the best performance indicated by the coefficient of determination (r2) and cross-validation coefficient of determination (Q2) values. We found that the value of r2 for train and test data are 0.84 and 0.82, respectively, and the value of Q2 is 0.81.
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References
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[26] C. W. Yap, "PaDEL-descriptor: An open source software to calculate molecular descriptors and fingerprints," Journal of Computational Chemistry, vol. 32, pp. 1466–1474, 2011, doi: 10.1002/jcc.21707.
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[35] J. F. Aranda, D. E. Bacelo, M. S. L. Aparicio, M. A. Ocsachoque, E. A. Castro, and P. R. Duchowicz, “Predicting the bioconcentration factor through a conformation-independent QSPR study,” SAR & QSAR in Environmental Research, vol. 28, pp. 749–763, 2017, doi: 10.1080/1062936X.2017.1377765.
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[2] P. M. Sharp and B. H. Hahn, "Origins of HIV and the AIDS Pandemic," Cold Spring Harbor Perspectives Medicine, vol. 1, pp. a006841–a006841, 2011, doi: 10.1101/cshperspect.a006841.
[3] GHO, "Number of deaths due to HIV/AIDS - Estimates by WHO region," GHO Data Repository. http://apps.who.int/gho/data/node.main.623?lang=en (accessed Sep. 09, 2019).
[4] Y. Wang, Z. Lv, and Y. Chu, "HIV protease inhibitors: a review of molecular selectivity and toxicity," HIVAIDS - Research and Palliative Care, vol. 7, p. 95, 2015, doi: 10.2147/HIV.S79956.
[5] A. Brik and C.-H. Wong, "HIV-1 protease: mechanism and drug discovery," Organic & Biomolecular Chemistry, vol. 1, pp. 5–14, 2003, doi: 10.1039/b208248a.
[6] Hospital Care for Children, "8.2 Pengobatan Antiretroviral (Antiretroviral therapy = ART) ICHRC," Hospital Care for Children. http://www.ichrc.org/82-pengobatan-antiretroviral-antiretroviral-therapy-art (accessed Sep. 09, 2019).
[7] E. Estrada, "On the Topological Sub-Structural Molecular Design (TOSS-MODE) in QSPR/QSAR and Drug Design Research," SAR & QSAR Environmental Research, vol. 11, pp. 55–73, 2000, doi: 10.1080/10629360008033229.
[8] A. P. Asmara, “Studi Qsar Senyawa Turunan Triazolopiperazin Amida Sebagai Inhibitor Enzim Dipeptidil Peptidase-IV (DPP IV) Menggunakan Metode Semiempirik AM,” Berkala Ilmiah MIPA, vol. 23, p. 9, 2013.
[9] I. Kurniawan, D. Tarwidi, and Jondri, "QSAR modeling of PTP1B inhibitor by using Genetic algorithm-Neural network methods," Journal of Physics: Conference Series, vol. 1192, p. 012059, Mar. 2019, doi: 10.1088/1742-6596/1192/1/012059.
[10] I. Kurniawan, M. Rosalinda, and N. Ikhsan, "Implementation of ensemble methods on QSAR Study of NS3 inhibitor activity as anti-dengue agent," SAR & QSAR Environmental Research, vol. 31, no. 6, pp. 477–492, Jun. 2020, doi: 10.1080/1062936X.2020.1773534.
[11] I. Kurniawan, M. S. Fareza, and P. Iswanto, "CoMFA, Molecular Docking and Molecular Dynamics Studies on Cycloguanil Analogues as Potent Antimalarial Agents," Indonesian Journal of Chemistry, vol. 21, no. 1, Art. no. 1, Sep. 2020, doi: 10.22146/ijc.52388.
[12] 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," in 2020 8th International Conference on Information and Communication Technology (ICoICT), Jun. 2020, pp. 1–4, doi: 10.1109/ICoICT49345.2020.9166158.
[13] 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," in 2020 6th International Conference on Interactive Digital Media (ICIDM), Dec. 2020, pp. 1–4, doi: 10.1109/ICIDM51048.2020.9339632.
[14] V. Ravichandran, V. K. Mourya, and R. K. Agrawal, "Prediction of HIV-1 protease inhibitory activity of 4-hydroxy-5,6-dihydropyran-2-ones: QSAR study," Journal of Enzyme Inhibition and Medicinal Chemistry, vol. 26, pp. 288–294, 2011, doi: 10.3109/14756366.2010.496364.
[15] N. Saranya and S. Selvaraj, "QSAR Studies on HIV-1 Protease Inhibitors Using Non-Linearly Transformed Descriptors," Current Computer-aided Drug Design, vol. 8, pp. 10–49, 2012, doi: 10.2174/157340912799218534.
[16] M. H. Fatemi, A. Heidari, and S. Gharaghani, "QSAR prediction of HIV-1 protease inhibitory activities using docking derived molecular descriptors," Journal of Theoretical Biology, vol. 369, pp. 13–22, 2015, doi: 10.1016/j.jtbi.2015.01.008.
[17] R. Darnag, B. Minaoui, and M. Fakir, "QSAR models for prediction study of HIV protease inhibitors using support vector machines, neural networks and multiple linear regression," Arabian Journal of Chemistry, vol. 10, pp. S600–S608, 2017, doi: 10.1016/j.arabjc.2012.10.021.
[18] S. Bhargava, N. Adhikari, S. A. Amin, K. Das, S. Gayen, and T. Jha, "Hydroxyethylamine derivatives as HIV-1 protease inhibitors: a predictive QSAR modeling study based on Monte Carlo optimization," SAR & QSAR Environmental Research, vol. 28, no. 12, pp. 973–990, Dec. 2017, doi: 10.1080/1062936X.2017.1388281.
[19] I. I. Baskin, V. A. Palyulin, and N. S. Zefirov, "Neural Networks in Building QSAR Models," in Artificial Neural Networks, vol. 458, New Jersey: Humana Press, 2006, pp. 133–154.
[20] R. Guha and P. C. Jurs, "Interpreting Computational Neural Network QSAR Models: A Measure of Descriptor Importance," Journal of Chemical Information and Modeling, vol. 45, pp. 800–806, 2005, doi: 10.1021/ci050022a.
[21] A.-L. Milac, S. Avram, and A.-J. Petrescu, "Evaluation of a neural networks QSAR method based on ligand representation using substituent descriptors," Journal of Molecular Graphics and Modelling, vol. 25, pp. 37–45, 2006, doi: 10.1016/j.jmgm.2005.09.014.
[22] S. Nagpal, S. Arora, S. Dey, and Shreya, "Feature Selection using Gravitational Search Algorithm for Biomedical Data," Procedia Computer Science, vol. 115, pp. 258–265, 2017, doi: 10.1016/j.procs.2017.09.133.
[23] S. A. Amin, N. Adhikari, S. Bhargava, T. Jha, and S. Gayen, "Structural exploration of hydroxyethylamines as HIV-1 protease inhibitors: new features identified," SAR & QSAR Environmental Research, vol. 29, pp. 385–408, 2018, doi: 10.1080/1062936X.2018.1447511.
[24] N. M. O'Boyle, M. Banck, C. A. James, C. Morley, T. Vandermeersch, and G. R. Hutchison, "Open Babel: An open chemical toolbox," Journal of Cheminformatics, vol. 3, p. 33, 2011, doi: 10.1186/1758-2946-3-33.
[25] H. Moriwaki, Y.-S. Tian, N. Kawashita, and T. Takagi, "Mordred: a molecular descriptor calculator," Journal of Cheminformatics, vol. 10, p. 4, 2018, doi: 10.1186/s13321-018-0258-y.
[26] C. W. Yap, "PaDEL-descriptor: An open source software to calculate molecular descriptors and fingerprints," Journal of Computational Chemistry, vol. 32, pp. 1466–1474, 2011, doi: 10.1002/jcc.21707.
[27] J. P. Papa et al., "Feature selection through gravitational search algorithm," in 2011 IEEE Int Conf Acoust Speech Signal Process (ICASSP), Prague, Czech Republic, 2011, pp. 2052–2055, doi: 10.1109/ICASSP.2011.5946916.
[28] "Newton’s law of gravitation,” Encyclopedia Britannica. Encyclopædia Britannica, inc., Accessed: Dec. 08, 2019. [Online]. Available: https://www.britannica.com/science/Newtons-law-of-gravitation.
[29] E. Rashedi, H. Nezamabadi-pour, and S. Saryazdi, “GSA: A Gravitational Search Algorithm,” Information Science, vol. 179, pp. 2232–2248, 2009, doi: 10.1016/j.ins.2009.03.004.
[30] E. Rashedi, H. Nezamabadi-pour, and S. Saryazdi, “BGSA: binary gravitational search algorithm,” Natural Computing, vol. 9, pp. 727–745, 2010, doi: 10.1007/s11047-009-9175-3.
[31] A. M. Al-Fakih, Z. Y. Algamal, M. H. Lee, M. Aziz, and H. T. M. Ali, “A QSAR model for predicting antidiabetic activity of dipeptidyl peptidase-IV inhibitors by enhanced binary gravitational search algorithm,” SAR & QSAR in Environmental Research, vol. 30, no. 6, pp. 403–416, Jun. 2019, doi: 10.1080/1062936X.2019.1607899.
[32] B. Sepehri and R. Ghavami, “Design of new CD38 inhibitors based on CoMFA modeling and molecular docking analysis of 4‑amino-8-quinoline carboxamides and 2,4-diamino-8-quinazoline carboxamides,” SAR & QSAR in Environmental Research, vol. 30, pp. 21–38, 2019, doi: 10.1080/1062936X.2018.1545695.
[33] A. Golbraikh and A. Tropsha, “Beware of q2!,” Journal of Molecular Graphics and Modelling, vol. 20, no. 4, pp. 269–276, Jan. 2002, doi: 10.1016/S1093-3263(01)00123-1.
[34] S. C. Peter, J. K. Dhanjal, V. Malik, N. Radhakrishnan, M. Jayakanthan, and D. Sundar, “Quantitative Structure-Activity Relationship (QSAR): Modeling Approaches to Biological Applications,” in Encyclopedia of Bioinformatics and Computational Biology, Elsevier, 2019, pp. 661–676.
[35] J. F. Aranda, D. E. Bacelo, M. S. L. Aparicio, M. A. Ocsachoque, E. A. Castro, and P. R. Duchowicz, “Predicting the bioconcentration factor through a conformation-independent QSPR study,” SAR & QSAR in Environmental Research, vol. 28, pp. 749–763, 2017, doi: 10.1080/1062936X.2017.1377765.
[36] P. Gramatica, “Principles of QSAR models validation: internal and external,” QSAR & Combinatorial Science, vol. 26, pp. 694–701, 2007, doi: 10.1002/qsar.200610151.
[37] “Descriptor List — mordred 1.2.1a1 documentation.” https://mordred-descriptor.github.io/documentation/master/descriptors.html (accessed Jan. 08, 2020).
[38] DEDUCT, “Database of Endocrine Disrupting chemicals and their Toxicity profiles.” https://cb.imsc.res.in/deduct/descriptors/eJaFhpFsbWo (accessed Nov. 24, 2019).
Published
2021-07-12
How to Cite
KURNIAWAN, Isman et al.
QSAR Study for Prediction of HIV-1 Protease Inhibitor Using the Gravitational Search Algorithm–Neural Network (GSA-NN) Methods.
Lontar Komputer : Jurnal Ilmiah Teknologi Informasi, [S.l.], v. 12, n. 2, p. 62-77, july 2021.
ISSN 2541-5832.
Available at: <https://ojs.unud.ac.id/index.php/lontar/article/view/70151>. Date accessed: 20 apr. 2024.
doi: https://doi.org/10.24843/LKJITI.2021.v12.i02.p01.
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