An Overview of Computer-aided Drug Design Tools and Recent Applications in Designing of Anti-diabetic Agents

Paranjeet       Kaur; Gopal       Khatik
doi:10.2174/1389450121666201119141525
Abstract

Background: In this fast-growing era, high throughput data is now being easily accessed by getting transformed into datasets which store the information. Such information is valuable to optimize the hypothesis and drug design via computer-aided drug design (CADD). Nowadays, we can explore the role of CADD in various disciplines like Nanotechnology, Biochemistry, Medical Sciences, Molecular Biology, etc.
Methods: We searched the valuable literature using a pertinent database with given keywords like computer-aided drug design, anti-diabetic, drug design, etc. We retrieved all valuable articles which are recent and discussing the role of computation in the designing of anti-diabetic agents.
Results: To facilitate the drug discovery process, the computational approach has set landmarks in the whole pipeline for drug discovery from target identification and mechanism of action to the identification of leads and drug candidates. Along with this, there is a determined endeavor to describe the significance of in-silico studies in predicting the absorption, distribution, metabolism, excretion, and toxicity profile. Thus, globally, CADD is accepted with a variety of tools for studying QSAR, virtual screening, protein structure prediction, quantum chemistry, material design, physical and biological property prediction.
Conclusion: Computer-assisted tools are used as the drug discovery tool in the area of different diseases, and here we reviewed the collaborative aspects of information technologies and chemoinformatic tools in the discovery of anti-diabetic agents, keeping in view the growing importance for treating diabetes.
Keywords: Antidiabetics, computer-aided drug design, molecular docking, QSAR, virtual screening, chemoinfor-matic tools.
« Previous Next »
Graphical Abstract

[1] 
Sharma SK, Sharma E, Sharma Y. A review: Recent computational approaches in medicinal chemistry: Computer aided drug designing and delivery. J Pharm Innov  2017; 6: 5-10.
[2] 
Wikberg J, Spjuth O, Eklund M, Lapins M. Chemoinformatics
   Taking Biology into Account: Proteochemometrics.Guha R,
   Bender A, editors Comput Approaches Cheminformatics
   Bioinforma. 2011th In: ed Hoboken: John Wiley & Sons.   2011; pp. 57-92.
[3] 
Reardon S. Project ranks billions of drug interactions. Nature  2013; 503(7477): 449-50.
[http://dx.doi.org/10.1038/503449a] [PMID: 24284710] 
[4] 
Hughes JP, Rees S, Kalindjian SB, Philpott KL. Principles of early drug discovery. Br J Pharmacol  2011; 162(6): 1239-49.
[http://dx.doi.org/10.1111/j.1476-5381.2010.01127.x PMID: 21091654] 
[5] 
Krasavin M, Karapetian R, Konstantinov I, et al. Discovery and potency optimization of 2-amino-5-arylmethyl-1,3-thiazole derivatives as potential therapeutic agents for prostate cancer. Arch Pharm (Weinheim)  2009; 342(7): 420-7.
[http://dx.doi.org/10.1002/ardp.200800201] [PMID: 19544302] 
[6] 
Firuzi LS. Curr Drug Targets  2014; 15: 1177-99.https://doi.org/http://dx.doi.org/10.2174/1389450115666141024113925
[http://dx.doi.org/10.2174/1389450115666141024113925] [PMID: 25341421] 
[7] 
Veselovsky AV, Zharkova MS, Poroikov VV, Nicklaus MC. Computer-aided design and discovery of protein-protein interaction inhibitors as agents for anti-HIV therapy. SAR QSAR Environ Res  2014; 25(6): 457-71.
[http://dx.doi.org/10.1080/1062936X.2014.898689 PMID: 24716798] 
[8] 
Song CM, Lim SJ, Tong JC. Recent advances in computer-aided drug design. Brief Bioinform  2009; 10(5): 579-91.https://doi.org/https://doi.org/10.1093/bib/bbp023
[http://dx.doi.org/10.1093/bib/bbp023] [PMID: 19433475] 
[9] 
Pârvu L. QSAR-a piece of drug design. J Cell Mol Med  2003; 7(3): 333-5.
[http://dx.doi.org/10.1111/j.1582-4934.2003.tb00235.x PMID: 14594559] 
[10] 
Nolte RT, Wisely GB, Westin S, et al. Ligand binding and co-activator assembly of the peroxisome proliferator-activated receptor-γ. Nature  1998; 395(6698): 137-43.
[http://dx.doi.org/10.1038/25931] [PMID: 9744270] 
[11] 
Rahman M, Karim M, Ahsan M, Khalipa A, Chowdhury M, Saifuzzaman M. Use of computer in drug design and drug discovery : a review. Int J Pharm Life Sci  2012; 1: 1-21.
[http://dx.doi.org/10.3329/ijpls.v1i2.12955] 
[12] 
Bharath EN, Manjula S, Vijaychand A. In silico drug design-tool for overcoming the innovation deficit in the drug discovery process. Int J Pharm Pharm Sci  2011; 3: 8-12.
[13] 
Sliwoski G, Kothiwale S, Meiler J, Lowe EW Jr. Computational methods in drug discovery. Pharmacol Rev  2013; 66(1): 334-95.
[http://dx.doi.org/10.1124/pr.112.007336] [PMID: 24381236] 
[14] 
Gilson MK, Zhou H-X. Calculation of protein-ligand binding affinities. Annu Rev Biophys Biomol Struct  2007; 36: 21-42.
[http://dx.doi.org/10.1146/annurev.biophys.36.040306.132550] [PMID: 17201676] 
[15] 
Yu W, MacKerell AD Jr. Computer-Aided Drug Design Methods. Methods Mol Biol  2017; 1520: 85-106.
[http://dx.doi.org/10.1007/978-1-4939-6634-9_5] [PMID: 27873247] 
[16] 
Prathipati P, Dixit A, Saxena A. Computer-Aided Drug Design: Integration of Structure-Based and Ligand-Based Approaches in Drug Design. Curr Comp-Aid Drug Des  2007; 3: 133-48.
[http://dx.doi.org/10.2174/157340907780809516] 
[17] 
Ou-Yang SS, Lu JY, Kong XQ, Liang ZJ, Luo C, Jiang H. Computational drug discovery. Acta Pharmacol Sin  2012; 33(9): 1131-40.
[http://dx.doi.org/10.1038/aps.2012.109] [PMID: 22922346] 
[18] 
Park H, Hwang KY, Kim YH, Oh KH, Lee JY, Kim K. Discovery and biological evaluation of novel α-glucosidase inhibitors with in vivo anti-diabetic effect. Bioorg Med Chem Lett  2008; 18(13): 3711-5.
[http://dx.doi.org/10.1016/j.bmcl.2008.05.056] [PMID: 18524587] 
[19] 
Huang HJ, Lee KJ, Yu HW, et al. Structure-based and ligand-based drug design for HER 2 receptor. J Biomol Struct Dyn  2010; 28(1): 23-37.
[http://dx.doi.org/10.1080/07391102.2010.10507341 PMID: 20476793] 
[20] 
Wold S, Dunn WJ. Multivariate quantitative structure-activity relationships (QSAR): conditions for their applicability. J Chem Inf Comput Sci  1983; 23: 6-13.
[http://dx.doi.org/10.1021/ci00037a002] 
[21] 
Anderson AC. The process of structure-based drug design. Chem Biol  2003; 10(9): 787-97.
[http://dx.doi.org/10.1016/j.chembiol.2003.09.002] [PMID: 14522049] 
[22] 
Scapin G. Structural biology and drug discovery. Curr Pharm Des  2006; 12(17): 2087-97.
[http://dx.doi.org/10.2174/138161206777585201] [PMID: 16796557] 
[23] 
Looger LL, Dwyer MA, Smith JJ, Hellinga HW. Computational design of receptor and sensor proteins with novel functions. Nature  2003; 423(6936): 185-90.
[http://dx.doi.org/10.1038/nature01556] [PMID: 12736688] 
[24] 
Kitchen DB, Decornez H, Furr JR, Bajorath J. Docking and scoring in virtual screening for drug discovery: methods and applications. Nat Rev Drug Discov  2004; 3(11): 935-49.
[http://dx.doi.org/10.1038/nrd1549] [PMID: 15520816] 
[25] 
Leach AR, Dolata DP, Prout K. Automated conformational analysis and structure generation: algorithms for molecular perception. J Chem Inf Comput Sci  1990; 30(3): 316-24.
[http://dx.doi.org/10.1021/ci00067a017] [PMID: 2211887] 
[26] 
Murrall N, Davies E. Conformational freedom in 3-D databases. 1. Techniques. J Chem Inf Comput Sci  1990; 30: 312-6.
[http://dx.doi.org/10.1021/ci00067a016] 
[27] 
Hurst T. Flexible 3D searching: the directed tweak technique. J Chem Inf Comput Sci  1994; 34: 190-6.
[http://dx.doi.org/10.1021/ci00017a025] 
[28] 
Moock T. Conformational Searching in ISIS/3D Databases. J Chem Inf Comput Sci  1994; 34: 184-9.
[http://dx.doi.org/10.1021/ci00017a024] 
[29] 
Náray-Szabó G, Ferenczy G. Molecular Electrostatics. Chem Rev  1995; 95: 829-47.
[http://dx.doi.org/10.1021/cr00036a002] 
[30] 
Carrupt P, Testa B, Gaillard P. In Reviews in Computational Chemistry.  Wiley-VCH. New York 1997; p. 241.
[31] 
Abraham D, Kellogg G. 3D-QSAR in drug design. Leiden: Escom 1993.
[32] 
Goodford PJ. A computational procedure for determining energetically favorable binding sites on biologically important macromolecules. J Med Chem  1985; 28(7): 849-57.
[http://dx.doi.org/10.1021/jm00145a002] [PMID: 3892003] 
[33] 
Saikia S, Bordoloi M. Molecular Docking: Challenges, Advances
   and its Use in Drug Discovery Perspective. Curr Drug Targets  2019; 20(5): 501-21. https://doi.org/http://dx.doi.org/10.2174/1389
  450119666181022153016
[http://dx.doi.org/10.2174/1389450119666181022153016] [PMID: 30360733] 
[34] 
Mohan V, Gibbs AC, Cummings MD, Jaeger EP, DesJarlais RL. Docking: successes and challenges. Curr Pharm Des  2005; 11(3): 323-33.
[http://dx.doi.org/10.2174/1381612053382106] [PMID: 15723628] 
[35] 
Bissantz C, Folkers G, Rognan D. Protein-based virtual screening of chemical databases. 1. Evaluation of different docking/scoring combinations. J Med Chem  2000; 43(25): 4759-67.
[http://dx.doi.org/10.1021/jm001044l] [PMID: 11123984] 
[36] 
Baig MH, Ahmad K, Hasan Q, et al. Interaction of Glucagon G-Protein Coupled Receptor with Known Natural Antidiabetic Compounds: Multiscoring In Silico Approach. Evid Based Complement Alternat Med 2015.2015497253
[http://dx.doi.org/10.1155/2015/497253] [PMID: 26236379] 
[37] 
Islam B, Sharma C, Adem A, Aburawi E, Ojha S. Insight into the mechanism of polyphenols on the activity of HMGR by molecular docking. Drug Des Devel Ther  2015; 9: 4943-51.
[http://dx.doi.org/10.2147/DDDT.S86705] [PMID: 26357462] 
[38] 
Peters MB, Raha K, Merz KMJ Jr. Quantum mechanics in structure-based drug design. Curr Opin Drug Discov Devel  2006; 9(3): 370-9.
[PMID: 16729734] 
[39] 
Sousa SF, Fernandes PA, Ramos MJ. Protein-ligand docking: current status and future challenges. Proteins  2006; 65(1): 15-26.
[http://dx.doi.org/10.1002/prot.21082] [PMID: 16862531] 
[40] 
Kramer B, Rarey M, Lengauer T. Evaluation of the FLEXX incremental construction algorithm for protein-ligand docking. Proteins  1999; 37(2): 228-41.
[http://dx.doi.org/10.1002/(SICI)1097-0134(19991101)37:2<228:AID-PROT8>3.0.CO;2-8 PMID: 10584068] 
[41] 
Forli S, Huey R, Pique ME, Sanner MF, Goodsell DS, Olson AJ. Computational protein-ligand docking and virtual drug screening with the AutoDock suite. Nat Protoc  2016; 11(5): 905-19.
[http://dx.doi.org/10.1038/nprot.2016.051] [PMID: 27077332] 
[42] 
Charifson PS, Corkery JJ, Murcko MA, Walters WP. Consensus scoring: A method for obtaining improved hit rates from docking databases of three-dimensional structures into proteins. J Med Chem  1999; 42(25): 5100-9.
[http://dx.doi.org/10.1021/jm990352k] [PMID: 10602695] 
[43] 
Huang S-Y, Grinter SZ, Zou X. Scoring functions and their evaluation methods for protein-ligand docking: recent advances and future directions. Phys Chem Chem Phys  2010; 12(40): 12899-908.
[http://dx.doi.org/10.1039/c0cp00151a] [PMID: 20730182] 
[44] 
Hansson T, Oostenbrink C, van Gunsteren W. Molecular dynamics simulations. Curr Opin Struct Biol  2002; 12(2): 190-6.
[http://dx.doi.org/10.1016/S0959-440X(02)00308-1 PMID: 11959496] 
[45] 
Frenkel D, Smit B. Understanding molecular simulation. In:  Academic
  press, Inc: san diego, CA.  2001.
[46] 
Allen M, Tilodesley DJ. Computer simulation of Liquids. Oxford, U.K: Oxford University Press 1989.
[47] 
Guido RVC, Oliva G, Andricopulo AD. Virtual screening and its integration with modern drug design technologies. Curr Med Chem  2008; 15(1): 37-46.
[http://dx.doi.org/10.2174/092986708783330683] [PMID: 18220761] 
[48] 
Hopfinger A, Tokarski J. Three-dimensional quantitative structure-activity relationship analysisPract Appl Comput drug Des.  New York: Marcel Dekker 1997; pp. 105-64.
[49] 
Hansch C. Quantitative approach to biochemical structure-activity relationships. Acc Chem Res  1969; 2: 232-9.
[http://dx.doi.org/10.1021/ar50020a002] 
[50] 
Hansch C, Fujita T. p-σ-π Analysis. A Method for the Correlation of Biological Activity and Chemical Structure. J Am Chem Soc  1964; 86: 1616-26.
[http://dx.doi.org/10.1021/ja01062a035] 
[51] 
Hopfinger AJ, Wang S, Tokarski JS, Jin B, Albuquerque M, Madhav PJ, et al. Construction of 3D-QSAR Models Using the 4D-QSAR Analysis Formalism. J Am Chem Soc  1997; 119: 10509-24.
[http://dx.doi.org/10.1021/ja9718937] 
[52] 
Patel HM, Noolvi MN, Sharma P, Jaiswal V, Bansal S, Lohan S, et al. Quantitative structure-activity relationship (QSAR) studies as strategic approach in drug discovery. Med Chem Res  2014; 23: 4991-5007.
[http://dx.doi.org/10.1007/s00044-014-1072-3] 
[53] 
Li WL, Zheng HC, Bukuru J, De Kimpe N. Natural medicines used in the traditional Chinese medical system for therapy of diabetes mellitus. J Ethnopharmacol  2004; 92(1): 1-21.
[http://dx.doi.org/10.1016/j.jep.2003.12.031] [PMID: 15099842] 
[54] 
Laitonjam W. Traditional medicinal plants of Manipur as anti-diabetics. J Med Plants Res  2011; 5: 677-87.
[55] 
Moses CR, Bronson SC. Newer Oral Anti-diabetic drugs  2016; 231-8.
[56] 
Kaur P, Mittal A, Nayak SK, Vyas M, Mishra V, Khatik GL. Current Strategies and Drug Targets in the Management of Type 2 Diabetes Mellitus. Curr Drug Targets  2018; 19(15): 1738-66.
[http://dx.doi.org/10.2174/1389450119666180727142902] [PMID: 30051787] 
[57] 
Deacon CF, Ahrén B, Holst JJ. Inhibitors of dipeptidyl peptidase IV: a novel approach for the prevention and treatment of Type 2 diabetes? Expert Opin Investig Drugs  2004; 13(9): 1091-102.
[http://dx.doi.org/10.1517/13543784.13.9.1091] [PMID: 15330741] 
[58] 
Mentlein R. Dipeptidyl-peptidase IV (CD26)--role in the inactivation of regulatory peptides. Regul Pept  1999; 85(1): 9-24.https://doi.org/https://doi.org/10.1016/S0167-0115(99)00089-0
[http://dx.doi.org/10.1016/S0167-0115(99)00089-0 PMID: 10588446] 
[59] 
Jayasree G, Mukkavalli S, Sahithi A. Docking studies of green tea flavonoids as insulin mimetics. Int J Comput Appl  2011; 30.
[60] 
Johnson TO, Ermolieff J, Jirousek MR. Protein tyrosine phosphatase 1B inhibitors for diabetes. Nat Rev Drug Discov  2002; 1(9): 696-709.
[http://dx.doi.org/10.1038/nrd895] [PMID: 12209150] 
[61] 
Ramachandran C, Kennedy BP. Protein tyrosine phosphatase 1B: a novel target for type 2 diabetes and obesity. Curr Top Med Chem  2003; 3(7): 749-57.
[http://dx.doi.org/10.2174/1568026033452276] [PMID: 12678842] 
[62] 
Tack CJJ, Smits P. Thiazolidinedione derivatives in type 2 diabetes mellitus. Neth J Med  2006; 64(6): 166-74.
[PMID: 16788214] 
[63] 
Cantello BCC, Cawthorne MA, Cottam GP, et al. [[omega-(Heterocyclylamino)alkoxy]benzyl]-2,4-thiazolidinediones as potent antihyperglycemic agents. J Med Chem  1994; 37(23): 3977-85.
[http://dx.doi.org/10.1021/jm00049a017] [PMID: 7966158] 
[64] 
Cohen NC. Guidebook on Molecular Modeling in Drug Design By N C Cohen. Academic Press 1995.
[65] 
Perun T, Propst C. Computer aided drug design 1999.
[66] 
Bibi S, Kalsoom S, Rashid H. In Silico Approach for Lead Identification and Optimization Of Antidiabetic Compounds. IOSR J Pharm Biol Sci  2013; 3: 36-46.
[67] 
Chauhan A, Sharma P, Srivastava P, Kumar N, Dudhe R. Plants Having Potential Anti-diabetic Activity. RE:view  2009; 2.
[68] 
Lipinski CA, Lombardo F, Dominy BW, Feeney PJ. Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings. Adv Drug Deliv Rev  2001; 46(1-3): 3-26. https://doi.org/https://doi.
  org/10.1016/S0169-409X(96)00423-1
[http://dx.doi.org/10.1016/S0169-409X(00)00129-0] [PMID: 11259830] 
[69] 
Berman HM, Westbrook J, Feng Z, et al. Protein data bank. Nucleic Acids Res  2000; 28(1): 235-42.
[http://dx.doi.org/10.1093/nar/28.1.235] [PMID: 10592235] 
[70] 
Weigelt J. Structural genomics-impact on biomedicine and drug discovery. Exp Cell Res  2010; 316(8): 1332-8.
[http://dx.doi.org/10.1016/j.yexcr.2010.02.041] [PMID: 20211166] 
[71] 
Ahmad F, Sudhanshu S, Naz H, Mahto M. Novel drug discovery for diabetes type-2 by pharmacophore, virtual screening and docking of PPARγ. Int J Res Pharm Life Sci  2016; 5: 2134-41.
[72] 
Kumaresan R. Structure Based Drug Designing for Diabetes
   Mellitus. J Proteomics Bioinform  2010; 03
[http://dx.doi.org/10.4172/jpb.1000157] 
[73] 
Taha MO, Habash M, Hatmal MM, Abdelazeem AH, Qandil A. Ligand-based modeling followed by in vitro bioassay yielded new potent glucokinase activators. J Mol Graph Model  2015; 56: 91-102.https://doi.org/https://doi.org/10.1016/j.jmgm.2014.12.003
[http://dx.doi.org/10.1016/j.jmgm.2014.12.003] [PMID: 25574766] 
[74] 
Lagos CF, Vecchiola A, Allende F, et al. Identification of novel 11β-HSD1 inhibitors by combined ligand- and structure-based virtual screening. Mol Cell Endocrinol  2014; 384(1-2): 71-82.https://doi.org/https://doi.org/10.1016/j.mce.2014.01.011
[http://dx.doi.org/10.1016/j.mce.2014.01.011] [PMID: 24447464] 
[75] 
Tanwar O, Tanwar L, Shaquiquzzaman M, Alam MM, Akhter M. Structure based virtual screening of MDPI database: discovery of structurally diverse and novel DPP-IV inhibitors. Bioorg Med Chem Lett  2014; 24(15): 3447-51.
[http://dx.doi.org/10.1016/j.bmcl.2014.05.076] [PMID: 24948564] 
[76] 
Balaramnavar VM, Srivastava R, Rahuja N, et al. Identification of novel PTP1B inhibitors by pharmacophore based virtual screening, scaffold hopping and docking. Eur J Med Chem  2014; 87: 578-94.https://doi.org/https://doi.org/10.1016/j.ejmech.2014.09.097
[http://dx.doi.org/10.1016/j.ejmech.2014.09.097] [PMID: 25299681] 
[77] 
Bamane R, Chitre T, Rakholiya V. Molecular docking studies of quinoline-3- carbohydrazide as novel PTP1B inhibitors as potential antihyperglycemic agents. Der Pharma Chem  2011; 3.
[78] 
Wu X, Hoffstedt J, Deeb W, et al. Depot-specific variation in protein-tyrosine phosphatase activities in human omental and subcutaneous adipose tissue: a potential contribution to differential insulin sensitivity. J Clin Endocrinol Metab  2001; 86(12): 5973-80.
[http://dx.doi.org/10.1210/jcem.86.12.8109] [PMID: 11739472] 
[79] 
Srinivasan P, Arumugam DS, Manikandan R, Arulvasu C. Molecular docking studies of 1, 2 disubstituted idopyranose from vitex negundo with anti-diabetic activity of type 2 diabetes. Int J Pharma Bio Sci  2011; 2: 68-83.
[80] 
Jiang C, Han S, Chen T, Chen J. 3D-QSAR and docking studies of arylmethylamine-based DPP IV inhibitors. Acta Pharm Sin B  2012; 2: 411-20.
[http://dx.doi.org/10.1016/j.apsb.2012.06.007] 
[81] 
Ahrén B, Hughes TE. Inhibition of dipeptidyl peptidase-4 augments insulin secretion in response to exogenously administered glucagon-like peptide-1, glucose-dependent insulinotropic polypeptide, pituitary adenylate cyclase-activating polypeptide, and gastrin-releasing peptide in mice. Endocrinology  2005; 146(4): 2055-9.
[http://dx.doi.org/10.1210/en.2004-1174] [PMID: 15604213] 
[82] 
Amuthalakshmi S, Arul Gnana Dhas AS. Insilico design of a ligand for DPP IV in type II diabetes. Adv Biol Res (Faisalabad)  2013; 7: 248-52.
[83] 
Kumar S, Khatik GL, Mittal A. Recent Developments in Sodium-Glucose Co-Transporter 2 (SGLT2) Inhibitors as a Valuable Tool in the Treatment of Type 2 Diabetes Mellitus. Mini Rev Med Chem  2020; 20(3): 170-82.
[http://dx.doi.org/10.2174/1389557519666191009163519] [PMID: 32134370] 
[84] 
Kumar S, Khatik GL, Mittal A. In silico Molecular Docking Study to Search New SGLT2 Inhibitor based on Dioxabicyclo[3.2.1] Octane Scaffold. Curr Comput Aided Drug Des  2020; 16(2): 145-54.
[http://dx.doi.org/10.2174/1573409914666181019165821] [PMID: 30345926] 
[85] 
Sharma MC, Sharma S. Molecular Modeling Studies of Thiophenyl C-Aryl Glucoside SGLT2 Inhibitors as Potential Anti-diabetic Agents. Int J Med Chem 2014.2014739646
[http://dx.doi.org/10.1155/2014/739646] [PMID: 25574393] 
[86] 
Walia V. Molecular Docking Studies of N-(2-Benzoylphenyl)-L-Tyrosine Derivatives with Anti-Diabetic Activity of Type 2 Diabetes. Pharmatutor 2015.
[87] 
Gautier JF, Fetita S, Sobngwi E, Salaün-Martin C. Biological actions of the incretins GIP and GLP-1 and therapeutic perspectives in patients with type 2 diabetes. Diabetes Metab  2005; 31(3 Pt 1): 233-42.https://doi.org/https://doi.org/10.1016/S1262-3636(07)70190-8
[http://dx.doi.org/10.1016/S1262-3636(07)70190-8 PMID: 16142014] 
[88] 
Prajapat R, Bhattacharya I. In-silicoStructure Modeling and Docking Studies Using Dipeptidyl Peptidase 4 (DPP4) Inhibitors against Diabetes Type-2. Adv Diabetes Metab  2016; 4: 73-84.
[http://dx.doi.org/10.13189/adm.2016.040403] 
[89] 
Bashary R, Khatik GL. Design, and facile synthesis of 1,3 diaryl-3-(arylamino)propan-1-one derivatives as the potential alpha-amylase inhibitors and antioxidants. Bioorg Chem  2019; 82: 156-62.
[http://dx.doi.org/10.1016/j.bioorg.2018.10.010] [PMID: 30321778] 
[90] 
Kumar P, Sruthi K, Pooja B, Bhanusree G, Fayaz KS. Design, synthesis, insilico molecular docking studies of some new anti-diabetic amino acid esters as potential targets for α- amylase. Mintage J Pharm Med Sci  2017; 6: 7-11.
[91] 
Ahmed D, Khan MI, Kaithwas G, Roy S, Gautam S, Singh M, et al. Molecular docking analysis and anti-diabetic activity of Rifabutin against STZ-NA induced diabetes in albino wistar rats. Beni-Suef Univ J Basic Appl Sci  2017; 6: 269-84.
[http://dx.doi.org/10.1016/j.bjbas.2017.04.010] 
[92] 
Colín-Lozano B, Estrada-Soto S, Chávez-Silva F, et al. Design, synthesis and in combo anti-diabetic bioevaluation of multitarget phenylpropanoic acids. Molecules  2018; 23(2): 1-16.
[http://dx.doi.org/10.3390/molecules23020340] [PMID: 29415496] 
[93] 
Patel AD, Barot R, Parmar I, et al. Molecular Docking, In-Silico ADMET Study and Development of 1,6- Dihydropyrimidine Derivative as Protein Tyrosine Phosphatase Inhibitor: An Approach to Design and Develop Anti-diabetic Agents. Curr Comput Aided Drug Des  2018; 14(4): 349-62.
[http://dx.doi.org/10.2174/1573409914666180426125721] [PMID: 29701158] 
[94] 
Abd El-Karim SS, Anwar MM, Syam YM, Nael MA, Ali HF, Motaleb MA. 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  2018; 81: 481-93.
[http://dx.doi.org/10.1016/j.bioorg.2018.09.021] [PMID: 30243239] 
[95] 
Asadollahi-Baboli M, Dehnavi S. Docking and QSAR analysis of tetracyclic oxindole derivatives as α-glucosidase inhibitors. Comput Biol Chem  2018; 76: 283-92.
[http://dx.doi.org/10.1016/j.compbiolchem.2018.07.019 PMID: 30103106] 
[96] 
Sun H, Zhang Y, Ding W, et al. Inhibitory activity evaluation and mechanistic studies of tetracyclic oxindole derivatives as α-glucosidase inhibitors. Eur J Med Chem  2016; 123: 365-78.
[http://dx.doi.org/10.1016/j.ejmech.2016.07.044] [PMID: 27487567] 
[97] 
Herrera-Rueda MÁ, Tlahuext H, Paoli P, et al. Design, synthesis, in vitro, in vivo and in silico pharmacological characterization of anti-diabetic N-Boc-l-tyrosine-based compounds. Biomed Pharmacother  2018; 108: 670-8.
[http://dx.doi.org/10.1016/j.biopha.2018.09.074] [PMID: 30245467] 
[98] 
Watterson KR, Hudson BD, Ulven T, Milligan G. Treatment of type 2 diabetes by free Fatty Acid receptor agonists. Front Endocrinol (Lausanne)  2014; 5: 137.
[http://dx.doi.org/10.3389/fendo.2014.00137] [PMID: 25221541] 
[99] 
Li Z, Xu X, Liu R, Deng F, Zeng X, Zhang L. Nitric oxide donor-based FFA1 agonists: Design, synthesis and biological evaluation as potential anti-diabetic and anti-thrombotic agents. Bioorg Med Chem  2018; 26(15): 4560-6.
[http://dx.doi.org/10.1016/j.bmc.2018.07.050] [PMID: 30082106] 
[100] 
Luthra T, Naga Lalitha K, Agarwal R, Uma A, Sen S. Design, synthesis and in vitro study of densely functionalized oxindoles as potent α-glucosidase inhibitors. Bioorg Med Chem  2018; 26(18): 4996-5005.
[http://dx.doi.org/10.1016/j.bmc.2018.08.022] [PMID: 30153956] 
[101] 
Mohammadi-Khanaposhtani M, Rezaei S, Khalifeh R, et al. Design, synthesis, docking study, α-glucosidase inhibition, and cytotoxic activities of acridine linked to thioacetamides as novel agents in treatment of type 2 diabetes. Bioorg Chem  2018; 80: 288-95.https://doi.org/https://doi.org/10.1016/j.bioorg.2018.06.035
[http://dx.doi.org/10.1016/j.bioorg.2018.06.035] [PMID: 29980114] 
[102] 
Nasli-Esfahani E, Mohammadi-Khanaposhtani M, Rezaei S, et al. A new series of Schiff base derivatives bearing 1,2,3-triazole: Design, synthesis, molecular docking, and α-glucosidase inhibition. Arch Pharm (Weinheim)  2019; 352(8)e1900034
[http://dx.doi.org/10.1002/ardp.201900034] [PMID: 31330079] 
[103] 
Feng X-Y, Jia W-Q, Liu X, et al. Identification of novel PPARα/γ dual agonists by pharmacophore screening, docking analysis, ADMET prediction and molecular dynamics simulations. Comput Biol Chem  2019; 78: 178-89.
[http://dx.doi.org/10.1016/j.compbiolchem.2018.11.023 PMID: 30557816] 
[104] 
Ibrahim MT, Uzairu A, Shallangwa GA, Ibrahim A. In-silico studies of some oxadiazoles derivatives as anti-diabetic compounds. J King Saud Univ-Sci  2020; 32: 423-32.https://doi.org/https://doi.org/10.1016/j.jksus.2018.06.006
[105] 
Kaur P, Bhat ZR, Bhat S, et al. Synthesis and evaluation of new 1,2,4-oxadiazole based trans- acrylic acid derivatives as potential PPAR-alpha/gamma dual agonist. Bioorg Chem 2020.100103867
[http://dx.doi.org/10.1016/j.bioorg.2020.103867] [PMID: 32353564] 
[106] 
Gejalakshmi S, Harikrishnan N, Thillia Govindrajan E, Divyasri A. Microwave assisted synthesis of tetrahydropyrmidine and in silico screening of anti-diabetic drug. Int J Curr Pharm Res  2020; 12.
[http://dx.doi.org/10.22159/ijcpr.2020v12i1.36821] 
[107] 
Banerjee M, Sahoo S, Sahu S. In silico designing and molecular docking studies on selected reported & proposed new compounds against ppar-γ receptor for type-2-diabetes. World J Pharm Pharm Sci  2016; 5: 1022-30.
[108] 
Kulkarni S, Gupta P, Pallavi A. Investigation of Enzymes Binding to “Voglibose- an Anti-diabetic Drug” and the Choice of Enzyme to be Used for Biosensing. Br J Pharm Res  2017; 14: 1-10.
[http://dx.doi.org/10.9734/BJPR/2016/30369] 
[109] 
Angadi KK, Gundampati R, Jagannadham MV, Kandru A. Molecular docking studies of guggultetrol from nymphaea pubescens with target glucokinase (GK) related to type-ii diabetes. J Appl Pharm Sci  2013; 3: 127-31.
[http://dx.doi.org/10.7324/JAPS.2013.30222] 
[110] 
Middha SK, Goyal AK, Faizan SA, Sanghamitra N, Basistha BC, Usha T. In silico-based combinatorial pharmacophore modelling and docking studies of GSK-3β and GK inhibitors of Hippophae. J Biosci  2013; 38(4): 805-14.
[http://dx.doi.org/10.1007/s12038-013-9367-y] [PMID: 24287660] 
[111] 
Goyal AK, Basistha BC, Sen A, Middha SK. Antioxidant profiling of Hippophae salicifolia growing in sacred forests of Sikkim, India. Funct Plant Biol  2011; 38(9): 697-701.
[http://dx.doi.org/10.1071/FP11016] [PMID: 32480925] 
[112] 
Bharti SK, Kumar A, Sharma NK, et al. Tocopherol from seeds of Cucurbita pepo against diabetes: validation by in vivo experiments supported by computational docking. J Formos Med Assoc  2013; 112(11): 676-90.
[http://dx.doi.org/10.1016/j.jfma.2013.08.003] [PMID: 24344360] 
[113] 
Jayasree G, Swarna S. Molecular docking studies of anti-diabetic activity of cinnamon compounds. Asian J Pharm Clin Res  2014; 7: 31-4.
[114] 
Kim SH, Hyun SH, Choung SY. Anti-diabetic effect of cinnamon extract on blood glucose in db/db mice. J Ethnopharmacol  2006; 104(1-2): 119-23.
[http://dx.doi.org/10.1016/j.jep.2005.08.059] [PMID: 16213119] 
[115] 
Esther GS, Manonmani AJ. Molecular docking studies of Ellagic acid and Gallic acid in diabetic nephropathy. Int J Drug Dev Res  2014; 6: 248-58.
[116] 
Singh DR. Morinda citrifolia L (Noni): A review of the scientific validation for its nutritional and therapeutic properties. J Diabetes Endocrinol  2012; 3: 77-91.
[http://dx.doi.org/10.5897/JDE10.006] 
[117] 
Awaluddin F, Mas Jaya Putra A, Supandi S. Molecular Docking Studies of Flavonoids of Noni Fruit (Morinda citrifolia L) to Peroxisome Proliferator-Activated Receptor-Gamma. PPAR 2015.
[http://dx.doi.org/10.2991/iccst-15.2015.18] 
[118] 
Jain A, Gupta P. In silico Comparative Molecular Docking Study and Analysis of Glycyrrhizin from Abrus precatorius (L.) against Antidiabetic Activity. European J Med Plants  2015; 6: 212-22.
[http://dx.doi.org/10.9734/EJMP/2015/13855] 
[119] 
Müller BM, Franz G. Chemical structure and biological activity of polysaccharides from Hibiscus sabdariffa. Planta Med  1992; 58(1): 60-7.
[http://dx.doi.org/10.1055/s-2006-961391] [PMID: 1620746] 
[120] 
Nerdy N. In silico docking of chemical compounds from Roselle Calyces (Hibiscus sabdariffa L.) as anti-diabetic. Int J Chemtech Res  2015; 7: 148-52.
[121] 
Shahinozzaman M, Taira N, Ishii T, Halim MA, Hossain MA, Tawata S. Anti-Inflammatory, Anti-Diabetic, and Anti-Alzheimer’s Effects of Prenylated Flavonoids from Okinawa Propolis: An Investigation by Experimental and Computational Studies. Molecules  2018; 23(10)E2479
[http://dx.doi.org/10.3390/molecules23102479] [PMID: 30262742] 
[122] 
Hossain MU, Khan MA, Rakib-Uz-Zaman SM, et al. Treating Diabetes Mellitus: Pharmacophore Based Designing of Potential Drugs from Gymnema sylvestre against Insulin Receptor Protein. BioMed Res Int 2016.20163187647
[http://dx.doi.org/10.1155/2016/3187647] [PMID: 27034931] 
[123] 
Bansode T, Salalkar B. Strategies In The Design Of Anti-diabetic Drugs From Terminalia Chebula Using In Silico And In Vitro Approach. MicroMedicine  2016; 4: 60-7.
[http://dx.doi.org/10.5281/zenodo.167869] 
[124] 
Davies JW, Glick M, Jenkins JL. Streamlining lead discovery by aligning in silico and high-throughput screening. Curr Opin Chem Biol  2006; 10(4): 343-51.
[http://dx.doi.org/10.1016/j.cbpa.2006.06.022] [PMID: 16822701] 
[125] 
Bharatam PV, Patel DS, Adane L, Mittal A, Sundriyal S. Modeling and informatics in designing anti-diabetic agents. Curr Pharm Des  2007; 13(34): 3518-30.
[http://dx.doi.org/10.2174/138161207782794239] [PMID: 18220788] 
[126] 
Paul SM, Mytelka DS, Dunwiddie CT, et al. How to improve R&D productivity: the pharmaceutical industry’s grand challenge. Nat Rev Drug Discov  2010; 9(3): 203-14.
[http://dx.doi.org/10.1038/nrd3078] [PMID: 20168317] 
Rights & Permissions Print Cite
Article Metrics
48
1
Journal Information
For Authors
For Editors
For Reviewers
Explore Articles
Open Access
Open Access Articles
For Visitors
DOI https://dx.doi.org/10.2174/1389450121666201119141525	Print ISSN 1389-4501
Publisher Name Bentham Science Publisher	Online ISSN 1873-5592
Current Drug Targets

An Overview of Computer-aided Drug Design Tools and Recent Applications in Designing of Anti-diabetic Agents

Abstract Play Pause

Graphical Abstract

Related Journals

Related Books

Related Articles

Abstract
