Implementation of ensemble methods on QSAR Study of NS3 inhibitor activity as anti-dengue agent

References

• S.B. Halstead and S.N. Cohen, Dengue hemorrhagic fever at 60 years: Early evolution of concepts of causation and treatment, Microbiol. Mol. Biol. Rev. 79 (2015), pp. 281–291. doi:10.1128/MMBR.00009-15.
   PubMed Web of Science ®Google Scholar
• A. Jaweria, F. Naeem, M. Malik, F. Javaid, Q. Ali, S. Ahmad, M.F. Khan, and I.A. Nasir, Dengue fever: Causes, prevention and recent advances, J. Mosq. Res. 6 (2016), pp. 1–9. doi:10.5376/jmr.2016.06.0029
   Google Scholar
• M. Guzman, Dengue and dengue hemorrhagic fever in the Americas: Lessons and challenges, J. Clin. Virol. 27 (2003), pp. 1–13. doi:10.1016/S1386-6532(03)00010-6.
   PubMed Web of Science ®Google Scholar
• K. Tantrakarnapa, Climatic factors influencing dengue hemorrhagic fever in Kolaka district, Indonesia, Environ. Nat. Resour. 16 (2018), pp. 1–10.
   Google Scholar
• S. Hasan, S.F. Jamdar, M. Alalowi, and S.M.A.A.A. Beaji, Dengue virus: A global human treat: Review of literature, J. Int. Soc. Prev. Commun. Dent. 6 (2016), pp. 1–6. doi:10.4103/2231-0762.175416.
   PubMed Web of Science ®Google Scholar
• Dengue and severe dengue. 2020. Available at https://www.who.int/news-room/fact-sheets/detail/dengue-and-severe-dengue
   Google Scholar
• O.J. Brady, P.W. Gething, S. Bhatt, J.P. Messina, J.S. Brownstein, A.G. Hoen, C.L. Moyes, A.W. Farlow, T.W. Scott, and S.I. Hay, Refining the global spatial limits of dengue virus transmission by evidence-based consensus, PLoS Negl. Trop. Dis. 6 (2012), pp. e1760. doi:10.1371/journal.pntd.0001760.
   PubMed Web of Science ®Google Scholar
• A.S. de Oliveira, M.L. da Silva, A.F.C.S. Oliveira, C.C. da Silva, R.R. Teixeira, and S.O. De Paula, NS3 and NS5 proteins: Important targets for anti-dengue drug design, J. Braz. Chem. Soc. 25 (2014), pp. 1759–1769.
   Web of Science ®Google Scholar
• H. Norazharuddin and N.S. Lai, Roles and prospects of dengue virus nonstructural proteins as antiviral targets: An easy digest, Malays. J. Med. Sci. 25 (2018), pp. 6–15. doi:10.21315/mjms2018.25.5.2.
   PubMed Web of Science ®Google Scholar
• C. Basavannacharya and S.G. Vasudevan, Suramin inhibits helicase activity of NS3 protein of dengue virus in a fluorescence-based high throughput assay format, Biochem. Biophys. Res. Commun. 453 (2014), pp. 539–544. doi:10.1016/j.bbrc.2014.09.113.
   PubMed Web of Science ®Google Scholar
• E.M. Silva, J.N. Conde, D. Allonso, G.T. Ventura, D.R. Coelho, P.H. Carneiro, M.L. Silva, M.V. Paes, K. Rabelo, G. Weissmuller, P.M. Bisch, and R.M. Borges, Dengue virus nonstructural 3 protein interacts directly with human glyceraldehyde-3-phosphate dehydrogenase (GAPDH) and reduces its glycolytic activity, Sci. Rep. 9 (2019), pp. 2651. doi:10.1038/s41598-019-39157-7.
   PubMed Web of Science ®Google Scholar
• K.-H. Lin, A. Ali, L. Rusere, D.I. Soumana, N.K. Yilmaz, and C.A. Schiffer, Dengue virus NS2B/NS3 protease inhibitors exploiting the prime side, J. Virol. 91 (2017). doi:10.1128/JVI.00045-17
   Web of Science ®Google Scholar
• S. Natarajan, NS3 protease from flavivirus as a target for designing antiviral inhibitors against dengue virus, Genet. Mol. Biol. 33 (2010), pp. 214–219. doi:10.1590/S1415-47572010000200002.
   PubMed Web of Science ®Google Scholar
• L. Abdel-ilah, E. Veljović, L. Gurbeta, and A. Badnjević, Applications of QSAR study in drug design, Int. J. Eng. Res. 6 (2017), pp. 7.
   Google Scholar
• P. Wang, L.-F. Li, Q.-Y. Wang, L.-Q. Shang, P.-Y. Shi, and Z. Yin, Anti-dengue-virus activity and structure-activity relationship studies of lycorine derivatives, Chem. Med. Chem. 9 (2014), pp. 1522–1533. doi:10.1002/cmdc.201300505.
   Google Scholar
• M. Awasthi, S.A. Amin, V. Shukla, S. Jain, U.K. Patil, and S. Gayen, Structural requirements of some derivatives based on natural alkaloid lycorine for their dengue inhibitory activity to accelerate dengue drug discovery efforts, Indian J. Nat. Prod. Res. 7 (2016), pp. 221–228.
   Web of Science ®Google Scholar
• I. Kurniawan and D. Tarwidi, QSAR modeling of PTP1B inhibitor by using genetic algorithm-neural network methods, J. Phys. Conf. Ser. 1192 (2019), pp. 012059. doi:10.1088/1742-6596/1192/1/012059.
   Google Scholar
• M. Hariono, E.E. Kamarulzaman, and H.A. Wahab, Computational design of dengue type-2 NS2B/NS3 protease inhibitor: 2D/3D QSAR of quinoline and its molecular docking, ICCST-3, Bali, Indonesia, 2015.
   Google Scholar
• S. Anusuya and M.M. Gromiha, Structural basis of flavonoids as dengue polymerase inhibitors: Insights from QSAR and docking studies, J. Biomol. Struct. Dyn. 37 (2019), pp. 104–115. doi:10.1080/07391102.2017.1419146.
   PubMed Web of Science ®Google Scholar
• M.W. Sarwar, A. Riaz, S.M.R. Dilshad, A. Al-Qahtani, M.S. Nawaz-Ul-Rehman, and M. Mubin, Structure activity relationship (SAR) and quantitative structure activity relationship (QSAR) studies showed plant flavonoids as potential inhibitors of dengue NS2B-NS3 protease, BMC. Struct. Biol. 18 (2018), pp. 6. doi:10.1186/s12900-018-0084-5.
   PubMed Web of Science ®Google Scholar
• 1.11. Ensemble methods — Scikit-learn 0.21.3 documentation. Available at https://scikit-learn.org/stable/modules/ensemble.html#random-forests.
   Google Scholar
• Random forests classifiers in python (article) - dataCamp. Available at https://www.datacamp.com/community/tutorials/random-forests-classifier-python.
   Google Scholar
• S. Wan and H. Yang, Comparison among methods of ensemble learning, ISBAST’13, Chengdu, Sichuan China, 2013.
   Google Scholar
• ChEMBL database. Available at https://www.ebi.ac.uk/chembl/.
   Google Scholar
• N.M. O’Boyle, M. Banck, C.A. James, C. Morley, T. Vandermeersch, and G.R. Hutchison, Open babel: An open chemical toolbox, J. Cheminform. 3 (2011), pp. 33.
   PubMed Web of Science ®Google Scholar
• H. Moriwaki, Y.-S. Tian, N. Kawashita, and T. Takagi, Mordred: A molecular descriptor calculator, J. Cheminform. 10 (2018), pp. 4. doi:10.1186/s13321-018-0258-y.
   PubMed Web of Science ®Google Scholar
• C.W. Yap, PaDEL-descriptor: An open source software to calculate molecular descriptors and fingerprints, J. Comput. Chem. 32 (2011), pp. 1466–1474. doi:10.1002/jcc.21707.
   PubMed Web of Science ®Google Scholar
• S. Nembrini, I.R. König, and M.N. Wright, The revival of the Gini importance? Bioinformatics 34 (2018), pp. 3711–3718. doi:10.1093/bioinformatics/bty373.
   PubMed Web of Science ®Google Scholar
• L. Breiman, Random forests, Mach. Learn. 45 (2001), pp. 5–32. doi:10.1023/A:1010933404324.
   Web of Science ®Google Scholar
• V. Svetnik, A. Liaw, C. Tong, J.C. Culberson, R.P. Sheridan, and B.P. Feuston, Random forest: A classification and regression tool for compound classification and QSAR modeling, J. Chem. Inf. Comput. Sci. 43 (2003), pp. 1947–1958. doi:10.1021/ci034160g.
   PubMedGoogle Scholar
• T.-K. An and M.-H. Kim, A new diverse AdaBoost classifier, AICI’10, Sanya, China, 2010.
   Google Scholar
• P. Geurts, D. Ernst, and L. Wehenkel, Extremely randomized trees, Mach. Learn. 63 (2006), pp. 3–42. doi:doi:10.1007/s10994-006-6226-1.
   Web of Science ®Google Scholar
• J. Zhu, T. Hastie, S. Rosset, and T. Hastie, Multi-class AdaBoost, Stat. Interface 2 (2009), pp. 349–360. doi:10.4310/SII.2009.v2.n3.a8.
   Web of Science ®Google Scholar
• M. Sokolova and G. Lapalme, A systematic analysis of performance measures for classification tasks, Inf. Process. Manag. 45 (2009), pp. 427–437. doi:10.1016/j.ipm.2009.03.002.
   Web of Science ®Google Scholar