A QSAR model for predicting antidiabetic activity of dipeptidyl peptidase-IV inhibitors by enhanced binary gravitational search algorithm

References

• G.A.R.Y. Suaifan, M.B. Shehadeh, R.M. Darwish, H. Al-Ijel, and V. Abbate, Design, synthesis and in vivo evaluation of novel glycosylated sulfonylureas as antihyperglycemic agents, Molecules 20 (2015), pp. 20063–20078. doi:10.3390/molecules201119676.
   PubMed Web of Science ®Google Scholar
• World Health Organization, Global Health Estimates: Deaths by Cause, Age, Sex, by Country and by Region, 2000–2016, World Health Organization, Geneva, 2018. Available at https://www.who.int/en/news-room/fact-sheets/detail/diabetes. (Accessed on 21 March 2019).
   Google Scholar
• K. Lin, Z. Cai, F. Wang, W. Zhang, and W. Zhou, Synthesis and biological evaluation of xanthine derivatives on dipeptidyl peptidase 4, Chem. Pharm. Bull. 61 (2013), pp. 477–482. doi:10.1248/cpb.c12-01046.
   PubMed Web of Science ®Google Scholar
• P. Zimmet, K.G.M.M. Alberti, and J. Shaw, Global and societal implications of the diabetes epidemic, Nature 414 (2001), pp. 782–787. doi:10.1038/414782a.
   PubMed Web of Science ®Google Scholar
• V.K. Vyas, H.G. Bhatt, P.K. Patel, J. Jalu, C. Chintha, N. Gupta, and M. Ghate, CoMFA and CoMSIA studies on C-aryl glucoside SGLT2 inhibitors as potential anti-diabetic agents, SAR QSAR Environ. Res. 24 (2013), pp. 519–551. doi:10.1080/1062936X.2012.751553.
   PubMed Web of Science ®Google Scholar
• J.H. Ahn, W.S. Park, M.A. Jun, M.S. Shin, S.K. Kang, K.Y. Kim, S. Dal Rhee, M.A. Bae, K.R. Kim, and S.G. Kim, Synthesis and biological evaluation of homopiperazine derivatives with β-aminoacyl group as dipeptidyl peptidase IV inhibitors, Bioorg. Med. Chem. Lett. 18 (2008), pp. 6525–6529. doi:10.1016/j.bmcl.2008.10.076.
   PubMed Web of Science ®Google Scholar
• S.S. Abd El-Karim, M.M. Anwar, Y.M. Syam, M.A. Nael, H.F. Ali, and M.A. Motaleb, Rational design and synthesis of new tetralin-sulfonamide derivatives as potent anti-diabetics and DPP-4 inhibitors: 2D & 3D QSAR, in vivo radiolabeling and bio distribution studies, Bioorg. Chem. 81 (2018), pp. 481–493. doi:10.1016/j.bioorg.2018.09.021.
   PubMed Web of Science ®Google Scholar
• Z. Amini, M.H. Fatemi, and S. Gharaghani, Hybrid docking-QSAR studies of DPP-IV inhibition activities of a series of aminomethyl-piperidones, Comput. Biol. Chem. 64 (2016), pp. 335–345. doi:10.1016/j.compbiolchem.2016.08.003.
   PubMed Web of Science ®Google Scholar
• S. Nordhoff, M. López-Canet, B. Hoffmann-Enger, S. Bulat, S. Cerezo-Gálvez, O. Hill, C. Rosenbaum, C. Rummey, M. Thiemann, V.G. Matassa, P.J. Edwards, and A. Feurer, From lead to preclinical candidate: Optimization of β-homophenylalanine based inhibitors of dipeptidyl peptidase IV, Bioorg. Med. Chem. Lett. 19 (2009), pp. 4818–4823. doi:10.1016/j.bmcl.2009.06.036.
   PubMed Web of Science ®Google Scholar
• C. Jiang, S. Han, T. Chen, and J. Chen, 3D-QSAR and docking studies of arylmethylamine-based DPP IV inhibitors, Acta Pharm. Sin. B 2 (2012), pp. 411–420. doi:10.1016/j.apsb.2012.06.007.
   Web of Science ®Google Scholar
• S. Paliwal, D. Seth, D. Yadav, R. Yadav, and S. Paliwal, Development of a robust QSAR model to predict the affinity of pyrrolidine analogs for dipeptidyl peptidase IV (DPP- IV), J. Enzym. Inhib. Med. Chem. 26 (2011), pp. 129–140. doi:10.3109/14756361003777057.
   PubMed Web of Science ®Google Scholar
• B.D. Patel and M.D. Ghate, 3D-QSAR studies of dipeptidyl peptidase-4 inhibitors using various alignment methods, Med. Chem. Res. 24 (2015), pp. 1060–1069. doi:10.1007/s00044-014-1178-7.
   Web of Science ®Google Scholar
• U. Saqib and M.I. Siddiqi, 3D-QSAR studies on triazolopiperazine amide inhibitors of dipeptidyl peptidase-IV as anti-diabetic agents, SAR QSAR Environ. Res. 20 (2009), pp. 519–535. doi:10.1080/10629360903278677.
   PubMed Web of Science ®Google Scholar
• M. Sharma, S. Jain, and R. Sharma, Trifluorophenyl-based inhibitors of dipeptidyl peptidase-IV as antidiabetic agents: 3D-QSAR COMFA, CoMSIA methodologies, Network Model. Anal. Health Inform. Bioinform. 7 (2018), pp. 1. doi:10.1007/s13721-017-0163-8.
   Web of Science ®Google Scholar
• E. Estrada, On the topological sub-structural molecular design (TOSS-MODE) in QSPR/QSAR and drug design research, SAR QSAR Environ. Res. 11 (2000), pp. 55–73. doi:10.1080/10629360008033229.
   PubMed Web of Science ®Google Scholar
• Z.Y. Algamal, M.K. Qasim, and H.T.M. Ali, A QSAR classification model for neuraminidase inhibitors of influenza A viruses (H1N1) based on weighted penalized support vector machine, SAR QSAR Environ. Res. 28 (2017), pp. 415–426. doi:10.1080/1062936X.2017.1326402.
   PubMed 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
• E. Rashedi, H. Nezamabadi-Pour, and S. Saryazdi, GSA: A gravitational search algorithm, Inf. Sci. 179 (2009), pp. 2232–2248. doi:10.1016/j.ins.2009.03.004.
   Web of Science ®Google Scholar
• B. Xing and W.-J. Gao, Gravitational search algorithm, in Innovative Computational Intelligence: A Rough Guide to 134 Clever Algorithms, Springer International Publishing, Cham, 2014, pp. 355–364.
   Google Scholar
• E. Rashedi, E. Rashedi, and H. Nezamabadi-Pour, A comprehensive survey on gravitational search algorithm, Swarm Evol. Comput. 41 (2018), pp. 141–158. doi:10.1016/j.swevo.2018.02.018.
   Web of Science ®Google Scholar
• V.K. Bohat and K.V. Arya, An effective gbest-guided gravitational search algorithm for real-parameter optimization and its application in training of feedforward neural networks, Knowledge-Based Syst. 143 (2018), pp. 192–207. doi:10.1016/j.knosys.2017.12.017.
   Web of Science ®Google Scholar
• B. Mohseni Bababdani and M. Mousavi, Gravitational search algorithm: A new feature selection method for QSAR study of anticancer potency of imidazo[4,5-b]pyridine derivatives, Chemom. Intell. Lab. Syst. 122 (2013), pp. 1–11. doi:10.1016/j.chemolab.2012.12.002.
   Web of Science ®Google Scholar
• S. Mirjalili, S.Z. Mohd Hashim, and H. Moradian Sardroudi, Training feedforward neural networks using hybrid particle swarm optimization and gravitational search algorithm, Appl. Math. Comput. 218 (2012), pp. 11125–11137.
   Web of Science ®Google Scholar
• E. Rashedi, H. Nezamabadi-Pour, and S. Saryazdi, BGSA: Binary gravitational search algorithm, Nat. Comput. 9 (2010), pp. 727–745. doi:10.1007/s11047-009-9175-3.
   Web of Science ®Google Scholar
• A.A. Ibrahim, A. Mohamed, and H. Shareef, Optimal power quality monitor placement in power systems using an adaptive quantum-inspired binary gravitational search algorithm, Int. J. Electr. Power Energy Syst. 57 (2014), pp. 404–413. doi:10.1016/j.ijepes.2013.12.019.
   Web of Science ®Google Scholar
• B. Ji, X. Yuan, Z. Chen, and H. Tian, Improved gravitational search algorithm for unit commitment considering uncertainty of wind power, Energy 67 (2014), pp. 52–62. doi:10.1016/j.energy.2014.02.014.
   Web of Science ®Google Scholar
• X. Yuan, B. Ji, S. Zhang, H. Tian, and Y. Hou, A new approach for unit commitment problem via binary gravitational search algorithm, Appl. Soft. Comput. 22 (2014), pp. 249–260. doi:10.1016/j.asoc.2014.05.029.
   Web of Science ®Google Scholar
• T. Baghgoli, M. Mousavi, and B. Mohseni Bababdani, Descriptor selection evaluation of binary gravitational search algorithm in quantitative structure-activity relationship studies of benzyl phenyl ether diamidine’s antiprotozoal activity and Chalcone’s anticancer potency, Chemom. Intell. Lab. Syst. 182 (2018), pp. 31–40. doi:10.1016/j.chemolab.2018.08.007.
   Web of Science ®Google Scholar
• M.J. Islam, X. Li, and Y. Mei, A time-varying transfer function for balancing the exploration and exploitation ability of a binary PSO, Appl. Soft. Comput. 59 (2017), pp. 182–196. doi:10.1016/j.asoc.2017.04.050.
   Web of Science ®Google Scholar
• M. Mafarja, I. Aljarah, A.A. Heidari, H. Faris, P. Fournier-Viger, X. Li, and S. Mirjalili, Binary dragonfly optimization for feature selection using time-varying transfer functions, Knowledge-Based Syst. 161 (2018), pp. 185–204. doi:10.1016/j.knosys.2018.08.003.
   Web of Science ®Google Scholar
• A. Nitta, H. Fujii, S. Sakami, M. Satoh, J. Nakaki, S. Satoh, H. Kumagai, and H. Kawai, Novel series of 3-amino-N-(4-aryl-1,1-dioxothian-4-yl)butanamides as potent and selective dipeptidyl peptidase IV inhibitors, Bioorg. Med. Chem. Lett. 22 (2012), pp. 7036–7040. doi:10.1016/j.bmcl.2012.09.099.
   PubMed Web of Science ®Google Scholar
• R. Todeschini, V. Consonni, A. Mauri, and M. Pavan, DRAGON, Software Version 6.0, Talete Srl, software, Milan, Italy, 2010. available at http://www.talete.mi.it/.
   Google Scholar
• V. Consonni, R. Todeschini, and M. Pavan, Structure/response correlations and similarity/diversity analysis by GETAWAY descriptors. 1. Theory of the novel 3D molecular descriptors, J. Chem. Inf. Comput. Sci 42 (2002), pp. 682–692.
   PubMedGoogle Scholar
• R. Todeschini and V. Consonni, Molecular Descriptors for Chemoinformatics, 2nd. ed., Vol. 41, Wiley-VCH, Weinheim, Germany, 2009.
   Google Scholar
• J. Caballero and M. Fernández, Linear and nonlinear modeling of antifungal activity of some heterocyclic ring derivatives using multiple linear regression and Bayesian-regularized neural networks, J. Mol. Model. 12 (2006), pp. 168–181. doi:10.1007/s00894-005-0014-x.
   PubMed Web of Science ®Google Scholar
• C. Rücker, G. Rücker, and M. Meringer, y-Randomization and its variants in QSPR/QSAR, J. Chem. Inf. Model. 47 (2007), pp. 2345–2357. doi:10.1021/ci700157b.
   PubMed Web of Science ®Google Scholar
• P. Gramatica, Principles of QSAR models validation: Internal and external, QSAR Comb. Sci. 26 (2007), pp. 694–701. doi:10.1002/(ISSN)1611-0218.
   Google Scholar