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2000
Volume 25, Issue 10
  • ISSN: 1568-0266
  • E-ISSN: 1873-4294

Abstract

Diabetes mellitus (DM) manifests as a complex and chronic metabolic disorder, posing a significant threat to global public health and contributing substantially to mortality rates. It is characterized by elevated blood glucose levels or hyperglycemia and requires effective preventive and therapeutic strategies. One promising approach involves targeting the inhibition of α-glucosidase and α-amylase, key enzymes responsible for carbohydrate hydrolysis. Inhibiting these enzymes proves beneficial in reducing postprandial glucose levels and mitigating postprandial hyperglycemia. However, existing antidiabetic medications are associated with undesirable side effects, highlighting the need to develop new molecules with increased efficacy and reduced side effects. Traditional methods for designing such molecules are often lengthy and costly. To address this, computer-based molecular modeling tools offer a promising approach to evaluate the anti-diabetic activities of chemical compounds. This review aims to compile information on chemical compounds assessed for their anti-diabetic activities through molecular modeling, with a particular focus on the period from 2020 to 2023.

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References

  1. American Diabetes Association. Standards of Medical Care in Diabetes—2011.2011Diabetes Care34S1S11S61
     [Google Scholar]
  2. MitraS. Finding structural requirements of structurally diverse α-glucosidase and α-amylase inhibitors through validated and predictive 2D-QSAR and 3D-QSAR analyses.J. Mol. Graph. Model.202412610864010.1016/j.jmgm.2023.108640
     [Google Scholar]
  3. AgarwalP. Alpha-amylase inhibition can treat diabetes mellitus.Res. Rev. J. Med. Heal. Sci.20165
     [Google Scholar]
  4. DeveciE. ÇayanF. Tel-ÇayanG. DuruM.E. Inhibitory activities of medicinal mushrooms on α-amylase and α-glucosidase-enzymes related to type 2 diabetes.S. Afr. J. Bot.2021137192310.1016/j.sajb.2020.09.039
     [Google Scholar]
  5. KhadayatK. MarasiniB.P. GautamH. GhajuS. ParajuliN. Evaluation of the alpha-amylase inhibitory activity of Nepalese medicinal plants used in the treatment of diabetes mellitus.Clinical Phytoscience2020613410.1186/s40816‑020‑00179‑8
     [Google Scholar]
  6. BhutkarM. BhiseS.B. In vitro assay of alpha amylase inhibitory activity of some indigenous plants.Int. J. Chem. Sci.20121045746210.31031/MAPP.2018.01.000518
     [Google Scholar]
  7. Klebe, G., Ed.; Drug Design: Methodology, Concepts, and Mode-of-Action.Berlin, HeidelbergSpringer Berlin Heidelberg2013
     [Google Scholar]
  8. SliwoskiG. KothiwaleS. MeilerJ. LoweE.W. Computational methods in drug discovery.Pharmacol. Rev.201466133439510.1124/pr.112.007336
     [Google Scholar]
  9. Prada-GraciaD. Huerta-YépezS. Moreno-VargasL.M. Application of computational methods for anticancer drug discovery, design, and optimization.Bol. Méd. Hosp. Infant. México2016736411423[English Edition].10.1016/j.bmhime.2017.11.040
     [Google Scholar]
  10. RomanoJ.D. TatonettiN.P. Informatics and Computational Methods in Natural Product Drug Discovery: A Review and Perspectives.Front. Genet.20191036810.3389/fgene.2019.00368
     [Google Scholar]
  11. AgarwalS. MehrotraR. An overview of Molecular Docking.JSM Chem2016421024
     [Google Scholar]
  12. AsiamahI. ObiriS.A. TamekloeW. ArmahF.A. BorquayeL.S. Applications of molecular docking in natural products-based drug discovery.Sci. Am.202320e0159310.1016/j.sciaf.2023.e01593
     [Google Scholar]
  13. YamariI. Oxidative functionalization of triterpenes isolated from Euphorbia resinifera latex: Semisynthesis, ADME-Tox, molecular docking, and molecular dynamics simulations.Chemical Physics Impact2023710037210.1016/j.chphi.2023.100372
     [Google Scholar]
  14. AbchirO. Structure-Based Virtual Screening, ADMET analysis, and Molecular Dynamics Simulation of Moroccan Natural Compounds as Candidates α-Amylase Inhibitors.ChemistrySelect2023826e20230109210.1002/slct.202301092
     [Google Scholar]
  15. AbchirO. Structure-based virtual screening, ADMET analysis, and molecular dynamics simulation of Moroccan natural compounds as candidates for the SARS-CoV-2 inhibitors.Nat. Prod. Res.2023001810.1080/14786419.2023.2281002
     [Google Scholar]
  16. MounadiN. NourH. ErrouguiA. TalbiM. ElKoualiM. ChtitaS. Discovery of Eugenol-derived Drug Candidates for the Treatment of COVID-19 by Applying Molecular Docking, Molecular Dynamics, and Pharmacokinetic Analysis.Physical Chemistry Research202412228930310.22036/pcr.2023.409956.2390
     [Google Scholar]
  17. AbchirO. Cannabis constituents as potential candidates against diabetes mellitus disease using molecular docking, dynamics simulations and ADMET investigations.Sci. Am.20232110.1016/j.sciaf.2023.e01745
     [Google Scholar]
  18. NourH. Exploring Cannabis sativa L for Anti-Alzheimer Potential: An extensive Computational Study including Molecular Docking, Molecular Dynamics, and ADMET Assessments.2024
     [Google Scholar]
  19. MounadiN. Computational Studies of Cannabis Derivatives as Potential Inhibitors of SARS-CoV-2 Mpro.Chemistry Africa2024mars10.1007/s42250‑024‑00914‑5
     [Google Scholar]
  20. NoreenS. SumrraS.H. ChohanZ.H. MustafaG. ImranM. Synthesis, characterization, molecular docking and network pharmacology of bioactive metallic sulfonamide-isatin ligands against promising drug targets.J. Mol. Struct.2023127713478010.1016/j.molstruc.2022.134780
     [Google Scholar]
  21. LeelanandaS.P. LindertS. Computational methods in drug discovery.Beilstein J. Org. Chem.2016122694271810.3762/bjoc.12.267
     [Google Scholar]
  22. HmamouchiR. BouachrineM. LakhlifiT. Tentative Pratique du Relation Quantitatives Structure-Activité/Propriété (QSAR/QSPR).Revue Interdisciplinaire2016111
     [Google Scholar]
  23. GolbraikhA. TropshaA. Beware of q2!J. Mol. Graph. Model.200220426927610.1016/s1093‑3263(01)00123‑1
     [Google Scholar]
  24. ChtitaS. Modélisation de molécules organiques hétérocycliques biologiquement actives par des méthodes QSAR/QSPR. Recherche de nouveaux médicaments. Chimie théorique et/ou physique, Université Moulay Ismaïl, Meknès, 2017. Français
     [Google Scholar]
  25. NantasenamatC. Isarankura-Na-AyudhyaC. NaennaT. PrachayasittikulV. A Practical Overview of Quantitative Structure-Activity Relationship.EXCLI J.20098748810.17877/DE290R‑690
     [Google Scholar]
  26. AbchirO. Insights into the inhibitory potential of novel hydrazinyl thiazole-linked indenoquinoxaline against alpha-amylase: a comprehensive QSAR, pharmacokinetic, and molecular modeling study.J. Biomol. Struct. Dyn.20240011810.1080/07391102.2024.2310778
     [Google Scholar]
  27. NourH. AbchirO. BelaidiS. QaisF.A. ChtitaS. BelaaouadS. 2D-QSAR and molecular docking studies of carbamate derivatives to discover novel potent anti-butyrylcholinesterase agents for Alzheimer’s disease treatment.Bull. Korean Chem. Soc.202243227729210.1002/bkcs.12449
     [Google Scholar]
  28. YamariI. AbchirO. NourH. El KoualiM. ChtitaS. Identification of new dihydrophenanthrene derivatives as promising anti-SARS-CoV-2 drugs through in silico investigations.Main Group Chem.202322346948410.3233/MGC‑220127
     [Google Scholar]
  29. AbchirO. Integrative Approach for Designing Novel Triazole Derivatives as α-Glucosidase Inhibitors: QSAR, Molecular Docking, ADMET, and Molecular Dynamics Investigations.Pharmaceuticals (Basel)202417226110.3390/ph17020261
     [Google Scholar]
  30. KhedraouiM. NourH. YamariI. AbchirO. ErrouguiA. ChtitaS. Design of a new potent Alzheimer’s disease inhibitor based on QSAR, molecular docking and molecular dynamics investigations.Chemical Physics Impact2023710036110.1016/j.chphi.2023.100361
     [Google Scholar]
  31. NourH. AbchirO. BelaidiS. ChtitaS. Research of new acetylcholinesterase inhibitors based on QSAR and molecular docking studies of benzene-based carbamate derivatives.Struct. Chem.20223361935194610.1007/s11224‑022‑01966‑4
     [Google Scholar]
  32. NourH. Design of Acetylcholinesterase Inhibitors as Promising Anti-Alzheimer’s Agents Based on QSAR, Molecular Docking, and Molecular Dynamics Studies of Liquiritigenin Derivatives.ChemistrySelect2023832e20230146610.1002/slct.202301466
     [Google Scholar]
  33. ShakerB. AhmadS. LeeJ. JungC. NaD. In silico methods and tools for drug discovery.Comput. Biol. Med.202113710485110.1016/j.compbiomed.2021.104851
     [Google Scholar]
  34. AlonsoH. BliznyukA.A. GreadyJ.E. Combining docking and molecular dynamic simulations in drug design.Med. Res. Rev.200626553156810.1002/med.20067
     [Google Scholar]
  35. WagnerJ.R. LeeC.T. DurrantJ.D. MalmstromR.D. FeherV.A. AmaroR.E. Emerging Computational Methods for the Rational Discovery of Allosteric Drugs.Chem. Rev.2016116116370639010.1021/acs.chemrev.5b00631
     [Google Scholar]
  36. De VivoM. MasettiM. BottegoniG. CavalliA. Role of Molecular Dynamics and Related Methods in Drug Discovery.J. Med. Chem.20165994035406110.1021/acs.jmedchem.5b01684
     [Google Scholar]
  37. HollingsworthS.A. DrorR.O. Molecular Dynamics Simulation for All.Neuron20189961129114310.1016/j.neuron.2018.08.011
     [Google Scholar]
  38. Ntie-KangF. An in silico evaluation of the ADMET profile of the StreptomeDB database.Springerplus2013235310.1186/2193‑1801‑2‑353
     [Google Scholar]
  39. VenkatramanV. FP-ADMET: a compendium of fingerprint-based ADMET prediction models.J. Cheminform.20211317510.1186/s13321‑021‑00557‑5
     [Google Scholar]
  40. ZhengM. LiuX. XuY. LiH. LuoC. JiangH. Computational methods for drug design and discovery: focus on China.Trends Pharmacol. Sci.2013341054955910.1016/j.tips.2013.08.004
     [Google Scholar]
  41. NorinderU. BergströmC.A.S. Prediction of ADMET Properties.ChemMedChem20061992093710.1002/cmdc.200600155
     [Google Scholar]
  42. KortagereS. EkinsS. Troubleshooting computational methods in drug discovery.J. Pharmacol. Toxicol. Methods2010612677510.1016/j.vascn.2010.02.005
     [Google Scholar]
  43. FujitaC. et ToshioH. p -σ-π Analysis. A Method for the Correlation of Biological Activity and Chemical Structure.J. Am. Chem. Soc.19648681616162610.1021/ja01062a035
     [Google Scholar]
  44. Nascimento,IJS. Applied Computer-Aided Drug Design: Models and Methods.Bentham Science Publishers2023
     [Google Scholar]
  45. CanaultB. Développement d’une plateforme de prédiction in silico des propriétés ADME-Tox.University of Orléans, 2018. French.2018
     [Google Scholar]
  46. TessaroF. Multiple applications of structure-based methods in drug discovery.
     [Google Scholar]
  47. MamadaM. Pérez-BolívarC. KumakiD. EsipenkoN.A. TokitoS. AnzenbacherP.Jr Benzimidazole Derivatives: Synthesis, Physical Properties, and n-Type Semiconducting Properties.Chemistry20142037118351184610.1002/chem.201403058
     [Google Scholar]
  48. PrestonP.N. Synthesis, reactions, and spectroscopic properties of benzimidazoles.Chem. Rev.197474327931410.1021/cr60289a001
     [Google Scholar]
  49. BelaidiS. MazriR. BelaidiH. LanezT. BouzidiD. Electronic Structure and Physico-Chemical Property Relationship for Thiazole Derivatives.Asian J. Chem.201325169241924510.14233/ajchem.2013.15199
     [Google Scholar]
  50. GuanQ. Triazoles in Medicinal Chemistry: Physicochemical Properties, Bioisosterism, and Application.J. Med. Chem.202467107788782410.1021/acs.jmedchem.4c00652
     [Google Scholar]
  51. ZafarW. AshfaqM. SumrraS.H. A review on the antimicrobial assessment of triazole-azomethine functionalized frameworks incorporating transition metals.J. Mol. Struct.2023128813574410.1016/j.molstruc.2023.135744
     [Google Scholar]
  52. ThummelR.P. KohliD.K. Preparation and properties of annelated pyridines.J. Org. Chem.197742162742274710.1021/jo00436a019
     [Google Scholar]
  53. ZarghiA. HajimahdiZ. Substituted oxadiazoles: a patent review (2010 – 2012).Expert Opin. Ther. Pat.20132391209123210.1517/13543776.2013.797409
     [Google Scholar]
  54. KaushikN.K. Biomedical Importance of Indoles.Molecules2013186610.3390/molecules18066620
     [Google Scholar]
  55. BermanH. HenrickK. NakamuraH. Announcing the worldwide Protein Data Bank.Nat. Struct. Mol. Biol.2003101298098010.1038/nsb1203‑980
     [Google Scholar]
  56. AbchirO. Design of novel benzimidazole derivatives as potential α-amylase inhibitors using QSAR, pharmacokinetics, molecular docking, and molecular dynamics simulation studies.J. Mol. Model.202228410610.1007/s00894‑022‑05097‑9
     [Google Scholar]
  57. BrzozowskiA.M. DaviesG.J. Structure of the Aspergillus oryzae α-Amylase Complexed with the Inhibitor Acarbose at 2.0 Å Resolution.Biochemistry19973636108371084510.1021/bi970539i
     [Google Scholar]
  58. SinghR. Parsing structural fragments of thiazolidin-4-one based α-amylase inhibitors: A combined approach employing in vitro colorimetric screening and GA-MLR based QSAR modelling supported by molecular docking, molecular dynamics simulation and ADMET studies.Comput. Biol. Med.202315710677610.1016/j.compbiomed.2023.106776
     [Google Scholar]
  59. MaurusR. Alternative Catalytic Anions Differentially Modulate Human α-Amylase Activity and Specificity.Biochemistry200847113332334410.1021/bi701652t
     [Google Scholar]
  60. GuptaS. BawejaG.S. GuptaG. AsatiV. Identification of potential N-substituted 5-benzylidenethiazolidine-2,4‑dione derivatives as α-amylase inhibitors: Computational cum synthetic studies.J. Mol. Struct.2023128713559610.1016/j.molstruc.2023.135596
     [Google Scholar]
  61. WilliamsL.K. The amylase inhibitor montbretin A reveals a new glycosidase inhibition motif.Nat. Chem. Biol.201511969169610.1038/nchembio.1865
     [Google Scholar]
  62. NaanaaiL. AissouqA.E. ZaitanH. BouachrineM. KhalilF. Computational study of 2-aryl quinoxaline derivatives as α-amylase inhibitors.Chemical Data Collections20234710107910.1016/j.cdc.2023.101079
     [Google Scholar]
  63. CockburnD.W. Molecular details of a starch utilization pathway in the human gut symbiont E ubacterium rectale.Mol. Microbiol.201595220923010.1111/mmi.12859
     [Google Scholar]
  64. El KhatabiK. Identification of Novel Indole Derivatives as Potent α- Amylase Inhibitors for the Treatment of Type-II Diabetes Using in-Silico Approaches.Biointerface Res. Appl. Chem.2023137610.33263/BRIAC131.076
     [Google Scholar]
  65. DahmaniR. ManachouM. BelaidiS. ChtitaS. BoughdiriS. Structural characterization and QSAR modeling of 1,2,4-triazole derivatives as α-glucosidase inhibitors.New J. Chem.20214531253126110.1039/D0NJ05298A
     [Google Scholar]
  66. YamamotoK. MiyakeH. KusunokiM. OsakiS. Crystal structures of isomaltase from Saccharomyces cerevisiae and in complex with its competitive inhibitor maltose.FEBS J.2010277204205421410.1111/j.1742‑4658.2010.07810.x
     [Google Scholar]
  67. KhaldanA. 3D-QSAR modeling, molecular docking and ADMET properties of benzothiazole derivatives as α-glucosidase inhibitors.Mater. Today Proc.2021457643765210.1016/j.matpr.2021.03.114
     [Google Scholar]
  68. BasriR. Synthesis, biological evaluation and molecular modelling of 3-Formyl-6-isopropylchromone derived thiosemicarbazones as α-glucosidase inhibitors.Bioorg. Chem.202313910673910.1016/j.bioorg.2023.106739
     [Google Scholar]
  69. RenL. Structural insight into substrate specificity of human intestinal maltase-glucoamylase.Protein Cell201121082783610.1007/s13238‑011‑1105‑3
     [Google Scholar]
  70. AminuK.S. UzairuA. AbechiS.E. ShallangwaG.A. UmarA.B. Activity prediction, structure-based drug design, molecular docking, and pharmacokinetic studies of 1,4-dihydropyridines derivatives as α-amylase inhibitors.J. Taibah Univ. Med. Sci.202419227028610.1016/j.jtumed.2023.12.003
     [Google Scholar]
  71. SimL. New Glucosidase Inhibitors from an Ayurvedic Herbal Treatment for Type 2 Diabetes: Structures and Inhibition of Human Intestinal Maltase-Glucoamylase with Compounds from Salacia reticulata.Biochemistry201049344345110.1021/bi9016457
     [Google Scholar]
  72. KhaldanA. BouamraneS. AjanaR. E. M. A. SbaiA. Moroccan Journal of Chemistry2022101110.48317/IMIST.PRSM/morjchem‑v10i1.31722
     [Google Scholar]
  73. SimL. Quezada-CalvilloR. SterchiE.E. NicholsB.L. RoseD.R. Human Intestinal Maltase–Glucoamylase: Crystal Structure of the N-Terminal Catalytic Subunit and Basis of Inhibition and Substrate Specificity.J. Mol. Biol.2008375378279210.1016/j.jmb.2007.10.069
     [Google Scholar]
  74. SaddiqueF.A. AhmadM. AshfaqU.A. MuddassarM. SultanS. ZakiM.E.A. Identification of Cyclic Sulfonamides with an N-Arylacetamide Group as α-Glucosidase and α-Amylase Inhibitors: Biological Evaluation and Molecular Modeling.Pharmaceuticals2022151110.3390/ph15010106
     [Google Scholar]
  75. NahoumV. Crystal structures of human pancreatic α-amylase in complex with carbohydrate and proteinaceous inhibitors.Biochem. J.2000346120120810.1042/bj3460201
     [Google Scholar]
  76. AliA. Dihydropyrazole Derivatives Act as Potent α-Amylase Inhibitors and Free Radical Scavengers: Synthesis, Bioactivity Evaluation, Structure–Activity Relationship, ADMET, and Molecular Docking Studies.ACS Omega2023823204122042210.1021/acsomega.3c00529
     [Google Scholar]
  77. AzimiF. Design and synthesis of novel quinazolinone-pyrazole derivatives as potential α-glucosidase inhibitors: Structure-activity relationship, molecular modeling and kinetic study.Bioorg. Chem.202111410512710.1016/j.bioorg.2021.105127
     [Google Scholar]
  78. LodgeJ.A. MaierT. LieblW. HoffmannV. SträterN. Crystal Structure of Thermotoga maritima α-Glucosidase AglA Defines a New Clan of NAD+-dependent Glycosidases.J. Biol. Chem.200327821191511915810.1074/jbc.M211626200
     [Google Scholar]
  79. LiM. LiL. LuL. XuX. HuJ. PengJ-B. Anti-α-Glucosidase, SAR Analysis, and Mechanism Investigation of Indolo[1,2-b]isoquinoline Derivatives.Molecules202328131310.3390/molecules28135282
     [Google Scholar]
  80. Roig-ZamboniV. Structure of human lysosomal acid α-glucosidase–a guide for the treatment of Pompe disease.Nat. Commun.201781111110.1038/s41467‑017‑01263‑3
     [Google Scholar]
  81. IrajiA. Shareghi-BrojeniD. MojtabaviS. FaramarziM.A. AkbarzadehT. SaeediM. Cyanoacetohydrazide linked to 1,2,3-triazole derivatives: a new class of α-glucosidase inhibitors.Sci. Rep.2022121110.1038/s41598‑022‑11771‑y
     [Google Scholar]
  82. KhanY. New quinoline-based triazole hybrid analogs as effective inhibitors of α-amylase and α-glucosidase: Preparation, in vitro evaluation, and molecular docking along with in silico studies.Front Chem.20221099582010.3389/fchem.2022.995820
     [Google Scholar]
  83. TagamiT. YamashitaK. OkuyamaM. MoriH. YaoM. KimuraA. Molecular Basis for the Recognition of Long-chain Substrates by Plant α-Glucosidases.J. Biol. Chem.201328826192961930310.1074/jbc.M113.465211
     [Google Scholar]
  84. KhanI. Synthesis and In vitro α-Amylase and α-Glucosidase Dual Inhibitory Activities of 1,2,4-Triazole-Bearing bis-Hydrazone Derivatives and Their Molecular Docking Study.ACS Omega2023825225082252210.1021/acsomega.3c00702
     [Google Scholar]
  85. CardonaF. Total Syntheses of Casuarine and Its 6‐ O ‐α‐Glucoside: Complementary Inhibition towards Glycoside Hydrolases of the GH31 and GH37 Families.Chemistry20091571627163610.1002/chem.200801578
     [Google Scholar]
  86. GaniR.S. Synthesis of novel indole, 1,2,4-triazole derivatives as potential glucosidase inhibitors.J. King Saud Univ. Sci.20203283388339910.1016/j.jksus.2020.09.026
     [Google Scholar]
  87. UllahH. BatoolT. NawazA. RahimF. KhanF. HussainA. Synthesis, in vitro α-glucosidase, α-amylase inhibitory potentials and molecular docking study of benzimidazole bearing sulfonamide analogues.Chemical Data Collections20234710107010.1016/j.cdc.2023.101070
     [Google Scholar]
  88. HussainR. Synthesis of Novel Benzimidazole-Based Thiazole Derivatives as Multipotent Inhibitors of α-Amylase and α-Glucosidase: In vitro Evaluation along with Molecular Docking Study.Molecules202227191910.3390/molecules27196457
     [Google Scholar]
  89. LohithaN. VijayakumarV. Imidazole Appended Novel Phenoxyquinolines as New Inhibitors of α-Amylase and α-Glucosidase Evidenced with Molecular Docking Studies.Polycycl. Aromat. Compd.20224285521553310.1080/10406638.2021.1939069
     [Google Scholar]
  90. YamamotoK. MiyakeH. KusunokiM. OsakiS. Steric hindrance by 2 amino acid residues determines the substrate specificity of isomaltase from Saccharomyces cerevisiae.J. Biosci. Bioeng.2011112654555010.1016/j.jbiosc.2011.08.016
     [Google Scholar]
  91. AliS. Novel 5-(Arylideneamino)-1H-Benzo[d]imidazole-2-thiols as Potent Anti-Diabetic Agents: Synthesis, In vitro α-Glucosidase Inhibition, and Molecular Docking Studies.ACS Omega2022748434684347910.1021/acsomega.2c03854
     [Google Scholar]
  92. UllahH. Benzimidazole Bearing Thiosemicarbazone Derivatives Act as Potent α-Amylase and α-Glucosidase Inhibitors; Synthesis, Bioactivity Screening and Molecular Docking Study.Molecules202227202010.3390/molecules27206921
     [Google Scholar]
  93. UllahH. In vitro α-glucosidase and α-amylase inhibitory potential and molecular docking studies of benzohydrazide based imines and thiazolidine-4-one derivatives.J. Mol. Struct.2021125113205810.1016/j.molstruc.2021.132058
     [Google Scholar]
  94. MehmoodR. Synthesis of Novel 2,3-Dihydro-1,5-Benzothiazepines as α-Glucosidase Inhibitors: In vitro, In vivo, Kinetic, SAR, Molecular Docking, and QSAR Studies.ACS Omega2022734302153023210.1021/acsomega.2c03328
     [Google Scholar]
  95. AbadanŞ. Synthesis and molecular modeling studies of naphthazarin derivatives as novel selective inhibitors of α-glucosidase and α-amylase.J. Mol. Struct.2023127813495410.1016/j.molstruc.2023.134954
     [Google Scholar]
  96. Bompard-GillesC. RousseauP. RougéP. PayanF. Substrate mimicry in the active center of a mammalian α amylase: structural analysis of an enzyme–inhibitor complex.Structure19964121441145210.1016/S0969‑2126(96)00151‑7
     [Google Scholar]
  97. ShadakshariA.J. Suresha KumaraT.H. KumarN. Jagadeep ChandraS. Anil KumarK.M. RamuR. Synthesis, characterization, and biocomputational assessment of the novel 3-hydroxy-4-(phenyl(pyridin-2-ylamino) methyl)-2-naphthoic acid derivatives as potential dual inhibitors of α-glucosidase and α-amylase enzymes.Results in Chemistry2023510074510.1016/j.rechem.2022.100745
     [Google Scholar]
  98. PashaA.R. Synthesis of new diphenyl urea-clubbed imine analogs and its Implications in diabetic management through in vitro and in silico approaches.Sci. Rep.2023131110.1038/s41598‑023‑28828‑1
     [Google Scholar]
  99. TretyakovaE. New Molecules of Diterpene Origin with Inhibitory Properties toward α-Glucosidase.Int. J. Mol. Sci.202223212110.3390/ijms232113535
     [Google Scholar]
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Advancements in Computational Approaches for Antidiabetic Drug Discovery: A Review
Curr. Top. Med. Chem. 25, 1123 (2025); https://doi.org/10.2174/0115680266311132240807065631
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  • Article Type:     Review Article
Keyword(s): Alpha-amylase; Alpha-glucosidase; Antidiabetes; Molecular modeling; QSAR; αlpha-amylase; αlpha-glucosidase

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