{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,30]],"date-time":"2026-09-30T21:05:24Z","timestamp":1790802324653,"version":"4.1.0"},"reference-count":181,"publisher":"International Association of Physical Chemists (IAPC)","license":[{"start":{"date-parts":[[2026,3,6]],"date-time":"2026-03-06T00:00:00Z","timestamp":1772755200000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100015690","name":"Universitas Padjadjaran","doi-asserted-by":"publisher","award":["BUPP Scholarship Year 2023\/2024"],"award-info":[{"award-number":["BUPP Scholarship Year 2023\/2024"]}],"id":[{"id":"10.13039\/501100015690","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["ADMET DMPK"],"abstract":"<jats:p>Background and purpose: Diabetes mellitus type 2 is a global health issue marked by hyperglycemia and metabolic dysfunction. Despite progress, discovering safe and effective antidiabetic agents remains crucial. This review highlights integrated In Silico, In Vitro, and in vivo methods for identifying novel antidiabetic compounds from natural and synthetic origins. Experimental approach: Computational tools including molecular docking, molecular dynamics, and ADMET prediction identified inhibitors targeting DPP-IV, \u03b1-glucosidase, and PPAR. Promising compounds underwent in vitro enzymatic and cellular assays, followed by in vivo efficacy tests in diabetic animal models assessing glucose levels, biochemical markers, and tissue histopathology. Key results: Integrated computational and experimental approaches effectively pinpointed compounds with strong target binding, enzyme inhibition, and positive cellular effects. In vivo data showed significant glucose reduction, enhanced insulin response, and pancreatic protection. ADMET analysis further supported their drug-likeness and safety profiles. Conclusion: Combining computational screening with biological validations forms a cost-effective pipeline for antidiabetic drug discovery. Multi-disciplinary integration increases lead identification success, guiding future refinement of in silico models and expanded in vivo studies to accelerate novel diabetes therapeutic development.<\/jats:p>","DOI":"10.5599\/admet.3070","type":"journal-article","created":{"date-parts":[[2026,3,6]],"date-time":"2026-03-06T13:19:15Z","timestamp":1772803155000},"page":"3070","source":"Crossref","is-referenced-by-count":4,"title":["&lt;i&gt;In silico&lt;\/i&gt; based exploration of natural and synthetic antidiabetic compounds: A comprehensive review of computational approaches"],"prefix":"10.5599","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0009-0005-6422-3136","authenticated-orcid":false,"given":"Ahmad Fariz","family":"Maulana","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3049-8375","authenticated-orcid":false,"given":"Sriwidodo","family":"Sriwidodo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8166-8421","authenticated-orcid":false,"given":"Iman Permana","family":"Maksum","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7807-4462","authenticated-orcid":false,"given":"Yaya","family":"Rukayadi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"3564","published-online":{"date-parts":[[2026,3,6]]},"reference":[{"key":"161982","doi-asserted-by":"crossref","unstructured":"[1] D. Lovic, A. Piperidou, I. Zografou, H. Grassos, A. Pittaras, A. Manolis. The Growing Epidemic of Diabetes Mellitus. Current Vascular Pharmacology 18 (2019) 104-109. https:\/\/doi.org\/10.2174\/1570161117666190405165911","DOI":"10.2174\/1570161117666190405165911"},{"key":"161983","doi-asserted-by":"crossref","unstructured":"[2] G. Rehman, M. Hamayun, A. Iqbal, S. Ul Islam, S. Arshad, K. Zaman, A. Ahmad, A. Shehzad, A. Hussain, I. Lee. In vitro antidiabetic effects and antioxidant potential of cassia nemophila pods. BioMed Research International 2018 (2018) 1824790. https:\/\/doi.org\/10.1155\/2018\/1824790","DOI":"10.1155\/2018\/1824790"},{"key":"161984","doi-asserted-by":"crossref","unstructured":"[3] O. Sakulkeo, C. Wattanapiromsakul, T. Pitakbut, S. Dej-Adisai. Alpha-Glucosidase Inhibition and Molecular Docking of Isolated Compounds from Traditional Thai Medicinal Plant, Neuropeltis racemosa Wall. Molecules 27 (2022) 639. https:\/\/doi.org\/10.3390\/molecules27030639","DOI":"10.3390\/molecules27030639"},{"key":"161985","doi-asserted-by":"crossref","unstructured":"[4] T. Htay, K. Soe, A. Lopez-Perez, A.H.A. Doan, M.A. Romagosa, K.K. Aung. Mortality and Cardiovascular Disease in Type 1 and Type 2 Diabetes. Current Cardiology Reports 21 (2019) 45. https:\/\/doi.org\/10.1007\/s11886-019-1133-9","DOI":"10.1007\/s11886-019-1133-9"},{"key":"161986","doi-asserted-by":"crossref","unstructured":"[5] S. Ahmed, S. Chandra, M. Najam-ul-haq, M. Younus. Biochemistry of Drug Resistance, Springer Nature Switzerland, Cham, Switzerland, 2021. https:\/\/doi.org\/10.1007\/978-3-030-76320-6","DOI":"10.1007\/978-3-030-76320-6"},{"key":"161987","doi-asserted-by":"crossref","unstructured":"[6] P. Patil, S. Mandal, S.K. Tomar, S. Anand. Food protein-derived bioactive peptides in management of type 2 diabetes. European Journal of Nutrition 54 (2015) 863-880. https:\/\/doi.org\/10.1007\/s00394-015-0974-2","DOI":"10.1007\/s00394-015-0974-2"},{"key":"161988","doi-asserted-by":"crossref","unstructured":"[7] J.B. Calixto. The role of natural products in modern drug discovery. Anais Da Academia Brasileira de Ciencias 91 (2019) e20190105. https:\/\/doi.org\/10.1590\/0001-3765201920190105","DOI":"10.1590\/0001-3765201920190105"},{"key":"161989","doi-asserted-by":"crossref","unstructured":"[8] A.O.H. Zayed. Exploring some marine \u2011 derived manzamine compounds for uterine fibroid treatment : molecular docking and dynamics studies. Discover Chemistry 2 (2025) 15. https:\/\/doi.org\/10.1007\/s44371-025-00088-0","DOI":"10.1007\/s44371-025-00088-0"},{"key":"161990","doi-asserted-by":"crossref","unstructured":"[9] S.A. Halim, S. Jabeen, A. Khan, A. Al-Harrasi. Rational design of novel inhibitors of \u03b1-glucosidase: An application of quantitative structure activity relationship and structure-based virtual screening. Pharmaceuticals 14 (2021) 482. https:\/\/doi.org\/10.3390\/ph14050482","DOI":"10.3390\/ph14050482"},{"key":"161991","doi-asserted-by":"crossref","unstructured":"[10] H. Meduru, Y.T. Wang, J.J.P. Tsai, Y.C. Chen. Finding a potential dipeptidyl peptidase-4 (DPP-4) inhibitor for type-2 diabetes treatment based on molecular docking, pharmacophore generation, and molecular dynamics simulation. International Journal of Molecular Sciences 17 (2016) 920. https:\/\/doi.org\/10.3390\/ijms17060920","DOI":"10.3390\/ijms17060920"},{"key":"161992","doi-asserted-by":"crossref","unstructured":"[11] S. Moretti. In silico experiments in scientific papers on molecular biology. Science & Technology Studies 24 (2011) 23-42. https:\/\/doi.org\/10.23987\/sts.55262","DOI":"10.23987\/sts.55262"},{"key":"161993","doi-asserted-by":"crossref","unstructured":"[12] L.G. Valerio. Application of advanced in silico methods for predictive modeling and information integration. Expert Opinion on Drug Metabolism and Toxicology 8 (2012) 395-398. https:\/\/doi.org\/10.1517\/17425255.2012.664636","DOI":"10.1517\/17425255.2012.664636"},{"key":"161994","doi-asserted-by":"crossref","unstructured":"[13] H.I. Ing\u00f3lfsson, C. Arnarez, X. Periole, S.J. Marrink. Computational \u201cmicroscopy\u201d of cellular membranes. Journal of Cell Science 129 (2016) 257-268. https:\/\/doi.org\/10.1242\/jcs.176040","DOI":"10.1242\/jcs.176040"},{"key":"161995","doi-asserted-by":"crossref","unstructured":"[14] M. Zloh, S.B. Kirton. The benefits of in silico modeling to identify possible small-molecule drugs and their off-target interactions. Future Medicinal Chemistry 10 (2018) 423-432. https:\/\/doi.org\/10.4155\/fmc-2017-0151","DOI":"10.4155\/fmc-2017-0151"},{"key":"161996","doi-asserted-by":"crossref","unstructured":"[15] T.F. Farooq, M. Holcik, A. MacKenzie. Spinal Muscular Atrophy: Classification, Diagnosis, Background, Molecular Mechanism and Development of Therapeutics. in: Neurodegener. Dis., IntechOpen, London, 2013: pp. 561-579. http:\/\/doi.org\/10.5772\/53800","DOI":"10.5772\/53800"},{"key":"161997","doi-asserted-by":"crossref","unstructured":"[16] N. Carlomagno, P. Incollingo, V. Tammaro, G. Peluso, N. Rupealta, G. Chiacchio, M.L. Sandoval Sotelo, G. Minieri, A. Pisani, E. Riccio, M. Sabbatini, U.M. Bracale, A. Calogero, C.A. Dodaro, M. Santangelo. Diagnostic, Predictive, Prognostic, and Therapeutic Molecular Biomarkers in Third Millennium: A Breakthrough in Gastric Cancer. BioMed Research International 2017 (2017) 7869802. https:\/\/doi.org\/10.1155\/2017\/7869802","DOI":"10.1155\/2017\/7869802"},{"key":"161998","doi-asserted-by":"crossref","unstructured":"[17] J.I. Johnsen, C. Dyberg, S. Fransson, M. Wickstr\u00f6m. Molecular mechanisms and therapeutic targets in neuroblastoma. Pharmacological Research 131 (2018) 164-176. https:\/\/doi.org\/10.1016\/j.phrs.2018.02.023","DOI":"10.1016\/j.phrs.2018.02.023"},{"key":"161999","doi-asserted-by":"crossref","unstructured":"[18] A. Najmi, M.S. Alam, N. Thangavel, M.M.E. Taha, A.M. Meraya, M. Albratty, H.A. Alhazmi, W. Ahsan, A. Haque, F. Azam. Synthesis, molecular docking, and in vivo antidiabetic evaluation of new benzylidene-2,4-thiazolidinediones as partial PPAR-\u03b3 agonists. Scientific Reports 13 (2023) 19869. https:\/\/doi.org\/10.1038\/s41598-023-47157-x","DOI":"10.1038\/s41598-023-47157-x"},{"key":"162000","doi-asserted-by":"crossref","unstructured":"[19] A. Ali. Development of antidiabetic drugs from benzamide derivatives as glucokinase activator: A computational approach. Saudi Journal of Biological Sciences 29 (2022) 3313-3325. https:\/\/doi.org\/10.1016\/j.sjbs.2022.01.058","DOI":"10.1016\/j.sjbs.2022.01.058"},{"key":"162001","doi-asserted-by":"crossref","unstructured":"[20] A.-R. Allouche. Gabedit\u2014A Graphical User Interface for Computational Chemistry Softwares. Journal of Computational Chemistry 32 (2010) 174-182. https:\/\/doi.org\/10.1002\/jcc.21600","DOI":"10.1002\/jcc.21600"},{"key":"162002","doi-asserted-by":"crossref","unstructured":"[21] V.E. Lamberti, L.D. Fosdick, E.R. Jessup, C.J.C. Schauble. A Hands-On Introduction to Molecular Dynamics. Journal of Chemical Education 79 (2002) 601. https:\/\/doi.org\/10.1021\/ed079p601","DOI":"10.1021\/ed079p601"},{"key":"162003","doi-asserted-by":"crossref","unstructured":"[22] A. Amberg. In Silico Methods. in: H.G. Vogel, J. Maas, F.J. Hock, D. Mayer (Eds.), Drug Discov. Eval. Saf. Pharmacokinet. Assays, Second Ed., 2nd ed., Springer, Berlin, 2013: pp. 1273-1296. https:\/\/doi.org\/10.1007\/978-3-642-25240-2_55","DOI":"10.1007\/978-3-642-25240-2_55"},{"key":"162004","doi-asserted-by":"crossref","unstructured":"[23] A.F.T. Gomes, W.F. de Medeiros, I. Medeiros, G. Piuvezam, J.K. da Silva-Maia, I.W.L. Bezerra, A.H. de A. Morais. In Silico Screening of Therapeutic Targets as a Tool to Optimize the Development of Drugs and Nutraceuticals in the Treatment of Diabetes mellitus: A Systematic Review. International Journal of Molecular Sciences 25 (2024) 9213. https:\/\/doi.org\/10.3390\/ijms25179213","DOI":"10.3390\/ijms25179213"},{"key":"162005","doi-asserted-by":"crossref","unstructured":"[24] Y. Chang, B.A. Hawkins, J.J. Du, P.W. Groundwater, D.E. Hibbs, F. Lai. A Guide to In Silico Drug Design. Pharmaceutics 15 (2023) 49. https:\/\/doi.org\/10.3390\/pharmaceutics15010049","DOI":"10.3390\/pharmaceutics15010049"},{"key":"162006","doi-asserted-by":"crossref","unstructured":"[25] A.G.A. Mushebenge, S.C. Ugbaja, N.A. Mbatha, R. B. Khan, H.M. Kumalo. Assessing the Potential Contribution of In Silico Studies in Discovering Drug Candidates That Interact with Various SARS-CoV-2 Receptors. International Journal of Molecular Sciences 24 (2023) 15518. https:\/\/doi.org\/10.3390\/ijms242115518","DOI":"10.3390\/ijms242115518"},{"key":"162007","doi-asserted-by":"crossref","unstructured":"[26] S.Y. Ugurlu, D. McDonald, H. Lei, A.M. Jones, S. Li, H.Y. Tong, M.S. Butler, S. He. Cobdock: an accurate and practical machine learning-based consensus blind docking method. Journal of Cheminformatics 16 (2024) 5. https:\/\/doi.org\/10.1186\/s13321-023-00793-x","DOI":"10.1186\/s13321-023-00793-x"},{"key":"162008","doi-asserted-by":"crossref","unstructured":"[27] O.A. Enejoh, C.H. Okonkwo, H. Nortey, O.A. Kemiki, A. Moses, F.N. Mbaoji, A.S. Yusuf, O.I. Awe. Machi\u00acne learning and molecular dynamics simulations predict potential TGR5 agonists for type 2 diabetes treatment. Frontiers in Chemistry 12 (2024) 1503593. https:\/\/doi.org\/10.3389\/fchem.2024.1503593","DOI":"10.3389\/fchem.2024.1503593"},{"key":"162009","doi-asserted-by":"crossref","unstructured":"[28] G.M. Morris, M. Lim-Wilby. Molecular docking. in: Mol. Model. Proteins, Humana Press, Totowa, NJ, United States, 2008: pp. 365-382. https:\/\/doi.org\/10.1007\/978-1-59745-177-2_19","DOI":"10.1007\/978-1-59745-177-2_19"},{"key":"162010","doi-asserted-by":"crossref","unstructured":"[29] J. Grzelczyk, G. Budryn, H. P\u00e9rez-S\u00e1nchez. Evaluation of affinity of bioactive isolates from various coffee extracts through binding with PPAR-\u03b3 with the use of isothermal titration calorimetry and docking simulation to prevent antidiabetic effects. Journal of Thermal Analysis and Calorimetry 142 (2020) 877-887. https:\/\/doi.org\/10.1007\/s10973-020-09801-w","DOI":"10.1007\/s10973-020-09801-w"},{"key":"162011","doi-asserted-by":"crossref","unstructured":"[30] N.U. Rehman, K. Rafiq, A. Khan, S.A. Halim, L. Ali, N. Al-Saady, A.H. Al-Balushi, H.K. Al-Busaidi, A. Al-Harrasi. \u03b1-Glucosidase inhibition and molecular docking studies of natural brominated metabolites from marine macro brown alga Dictyopteris hoytii. Marine Drugs 17 (2019) 666. https:\/\/doi.org\/10.3390\/md17120666","DOI":"10.3390\/md17120666"},{"key":"162012","doi-asserted-by":"crossref","unstructured":"[31] N.T. Issa, E. V Badiavas, S. Sch\u00fcrer. Research Techniques Made Simple : Molecular Docking in Dermatology - A Foray into In Silico Drug Discovery. Journal of Investigative Dermatology 139 (2019) 2400-2409. https:\/\/doi.org\/10.1016\/j.jid.2019.06.129","DOI":"10.1016\/j.jid.2019.06.129"},{"key":"162013","doi-asserted-by":"crossref","unstructured":"[32] E.F. Pettersen, T.D. Goddard, C.C. Huang, G.S. Couch, D.M. Greenblatt, E.C. Meng, T.E. Ferrin. UCSF Chimera \u2014 A Visualization System for Exploratory Research and Analysis. Journal of Computational Chemistry 25 (2004) 1605-1612. https:\/\/doi.org\/10.1002\/jcc.20084","DOI":"10.1002\/jcc.20084"},{"key":"162014","doi-asserted-by":"crossref","unstructured":"[33] D. Van Der Spoel, E. Lindahl, B. Hess, G. Groenhof. GROMACS: Fast, Flexible, and Free. Journal of Computational Chemistry 26 (2005) 1701-1718. https:\/\/doi.org\/10.1002\/jcc.20291","DOI":"10.1002\/jcc.20291"},{"key":"162015","doi-asserted-by":"crossref","unstructured":"[34] D. Seeliger, B.L. De Groot, V. Pymol. Ligand docking and binding site analysis with PyMOL and Autodock \/ Vina. Journal of Computer-Aided Molecular Design 24 (2010) 417-422. https:\/\/doi.org\/10.1007\/s10822-010-9352-6","DOI":"10.1007\/s10822-010-9352-6"},{"key":"162016","doi-asserted-by":"crossref","unstructured":"[35] G.M. Clore, C.D. Schwieters. Docking of Protein - Protein Complexes on the Basis of Highly Ambiguous Intermolecular Distance Restraints Derived from 1 H \/ 15 N Chemical Shift Mapping and Backbone 15 N - 1 H Residual Dipolar Couplings Using Conjoined Rigid Body \/ Torsion Angle Dynamic. Journal of the American Chemical Society 125 (2003) 2902-2912. https:\/\/doi.org\/10.1021\/ja028893d","DOI":"10.1021\/ja028893d"},{"key":"162017","doi-asserted-by":"crossref","unstructured":"[36] J.J. Irwin, K.G. Tang, J. Young, C. Dandarchuluun, B.R. Wong, M. Khurelbaatar, Y.S. Moroz, J. Mayfield, R.A. Sayle. ZINC20 - A Free Ultra Large-Scale Chemical Database for Ligand Discovery. Journal of Chemical Information and Modeling 60 (2020) 6065-6073. https:\/\/doi.org\/10.1021\/acs.jcim.0c00675","DOI":"10.1021\/acs.jcim.0c00675"},{"key":"162018","doi-asserted-by":"crossref","unstructured":"[37] B.I. Tingle, K.G. Tang, M. Castanon, J.J. Gutierrez, M. Khurelbaatar, C. Dandarchuluun, Y.S. Moroz, J.J. Irwin. ZINC-22 - A Free Multi-Billion-Scale Database of Tangible Compounds for Ligand Discovery. Journal of Chemical Information and Modeling 63 (2023) 1166-1176. https:\/\/doi.org\/10.1021\/acs.jcim.2c01253","DOI":"10.1021\/acs.jcim.2c01253"},{"key":"162019","doi-asserted-by":"crossref","unstructured":"[38] R.E. Amaro, J. Baudry, J. Chodera, O. Demir, J.A. McCammon, Y. Miao, J.C. Smith. Biophysical Perspective Ensemble Docking in Drug Discovery. Biophysical Perspective 114 (2018) 2271-2278. https:\/\/doi.org\/10.1016\/j.bpj.2018.02.038","DOI":"10.1016\/j.bpj.2018.02.038"},{"key":"162020","doi-asserted-by":"crossref","unstructured":"[39] K. Mckay, N.B. Hamilton, J.M. Remington, S.T. Schneebeli. Essential Dynamics Ensemble Docking for Structure-Based GPCR Drug Discovery. Frontiers in Molecular Biosciences 9 (2022) 879212. https:\/\/doi.org\/10.3389\/fmolb.2022.879212","DOI":"10.3389\/fmolb.2022.879212"},{"key":"162021","doi-asserted-by":"crossref","unstructured":"[40] W.E. Falcon, S.R. Ellingson, J.C. Smith, J. Baudry. Ensemble Docking in Drug Discovery: How Many Protein Configurations from Molecular Dynamics Simulations are Needed To Reproduce Known Ligand Binding? The Journal of Physical Chemistry B 123 (2019) 5189-5195. https:\/\/doi.org\/10.1021\/acs.jpcb.8b11491","DOI":"10.1021\/acs.jpcb.8b11491"},{"key":"162022","doi-asserted-by":"crossref","unstructured":"[41] I.P. Maksum, R. Rustaman, Y. Deawati, Y. Rukayadi, A.R. Utami, Z.K. Nafisa. Study of the antidiabetic mechanism of berberine compound on FOXO1 transcription factor through molecular docking and molecular dynamics simulations. Journal of Molecular Modeling 30 (2024) 260. https:\/\/doi.org\/10.1007\/s00894-024-06060-6","DOI":"10.1007\/s00894-024-06060-6"},{"key":"162023","doi-asserted-by":"crossref","unstructured":"[42] I.P. Maksum, A.F. Maulana, M. Yusuf, R. Mulyani, W. Destiarani, R. Rustaman. Molecular Dynamics Simulation of a tRNA-Leucine Dimer with an A3243G Heteroplasmy Mutation in Human Mitochondria Using a Secondary Structure Prediction Approach. Indonesian Journal of Chemistry 22 (2022) 1043-1051. https:\/\/doi.org\/10.22146\/ijc.72774","DOI":"10.22146\/ijc.72774"},{"key":"162024","doi-asserted-by":"crossref","unstructured":"[43] W. Destiarani, R. Mulyani, M. Yusuf, I.P. Maksum. Molecular Dynamics Simulation of T10609C and C10676G Mutations of Mitochondrial ND4L Gene Associated With Proton Translocation in Type 2 Diabetes Mellitus and Cataract Patients. Bioinformatics and Biology Insights 14 (2020) 1177932220978672. https:\/\/doi.org\/10.1177\/1177932220978672","DOI":"10.1177\/1177932220978672"},{"key":"162025","doi-asserted-by":"crossref","unstructured":"[44] B. Geoffrey, A. Sanker, R. Madaj, M.S.V. Tresanco, M. Upadhyay, J. Gracia. A program to automate the discovery of drugs for West Nile and Dengue virus \u2014 programmatic screening of over a billion compounds on PubChem , generation of drug leads and automated in silico modelling. Journal of Biomolecular Structure and Dynamics 40 (2020) 4293-4300. https:\/\/doi.org\/10.1080\/07391102.2020.1856185","DOI":"10.1080\/07391102.2020.1856185"},{"key":"162026","doi-asserted-by":"crossref","unstructured":"[45] M. Orlandi, Y. Geng, M. Macchiagodena, M. Pagliai, P. Procacci. Solvation Free Energies of Drug-like Molecules via Fast Growth in an Explicit Solvent: Assessment of the AM1-BCC, RESP\/HF\/6\u221231G*, RESP-QM\/MM, and ABCG2 Fixed-Charge Approaches. Journal of Chemical Theory and Computation 21 (2025) 7977-7990. https:\/\/pubs.acs.org\/doi\/full\/10.1021\/acs.jctc.5c00749","DOI":"10.1021\/acs.jctc.5c00749"},{"key":"162027","doi-asserted-by":"crossref","unstructured":"[46] M. Buttenschoen, G.M. Morris, C.M. Deane. PoseBusters: AI-based docking methods fail to generate physically valid poses or generalise to novel sequences. Chemical Science 15 (2024) 3130-3139. https:\/\/doi.org\/10.1039\/D3SC04185A","DOI":"10.1039\/D3SC04185A"},{"key":"162028","doi-asserted-by":"crossref","unstructured":"[47] A. Hospital, J.R. Go\u00f1i, M. Orozco, J.L. Gelp\u00ed. Molecular Dynamics Simulations and Flow. Advances and Applications in Bioinformatics and Chemistry 8 (2015) 37-47. https:\/\/doi.org\/10.2147\/AABC.S70333","DOI":"10.2147\/AABC.S70333"},{"key":"162029","doi-asserted-by":"crossref","unstructured":"[48] R. Agrawal, P. Jain, S. Narayan Dikshit. Ligand-Based Pharmacophore Detection, Screening of Potential Gliptins and Docking Studies to Get Effective Antidiabetic Agents. Combinatorial Chemistry & High Throughput Screening 15 (2014) 849-876. https:\/\/doi.org\/10.2174\/138620712803901090","DOI":"10.2174\/138620712803901090"},{"key":"162030","doi-asserted-by":"crossref","unstructured":"[49] M.F. Khan, G. Verma, W. Akhtar, M. Shaquiquzzaman, M. Akhter, M.A. Rizvi, M.M. Alam. Pharmacophore modeling, 3D-QSAR, docking study and ADME prediction of acyl 1,3,4-thiadiazole amides and sulfonamides as antitubulin agents. Arabian Journal of Chemistry 12 (2016) 5000-5018. http:\/\/dx.doi.org\/10.1016\/j.arabjc.2016.11.004","DOI":"10.1016\/j.arabjc.2016.11.004"},{"key":"162031","doi-asserted-by":"crossref","unstructured":"[50] M.T. Muhammed, E. Aki-Yalcin. Pharmacophore Modeling in Drug Discovery : Methodology and Current Status. Journal of the Turkish Chemical Society 8 (2021) 749-762. https:\/\/doi.org\/10.18596\/jotcsa.927426","DOI":"10.18596\/jotcsa.927426"},{"key":"162032","doi-asserted-by":"crossref","unstructured":"[51] Y. Podolyan, G. Karypis. Common Pharmacophore Identification Using Frequent Clique Detection Algorithm. Journal of Chemical Information and Modeling 49 (2009) 13-21. https:\/\/doi.org\/10.1021\/ci8002478","DOI":"10.1021\/ci8002478"},{"key":"162033","doi-asserted-by":"crossref","unstructured":"[52] D. Schaller, D. \u0160ribar, T. Noonan, L. Deng, T.N. Nguyen, S. Pach, D. Machalz, M. Bermudez, G. Wolber. Next generation 3D pharmacophore modeling. Wiley Interdisciplinary Reviews: Computational Molecular Science 10 (2020) 1468. https:\/\/doi.org\/10.1002\/wcms.1468","DOI":"10.1002\/wcms.1468"},{"key":"162034","doi-asserted-by":"crossref","unstructured":"[53] I.L. Lu, K.C. Tsai, Y.K. Chiang, W.T. Jiaang, S.H. Wu, N. Mahindroo, C.H. Chien, S.J. Lee, X. Chen, Y.S. Chao, S.Y. Wu. A three-dimensional pharmacophore model for dipeptidyl peptidase IV inhibitors. European Journal of Medicinal Chemistry 43 (2008) 1603-1611. https:\/\/doi.org\/10.1016\/j.ejmech.2007.11.014","DOI":"10.1016\/j.ejmech.2007.11.014"},{"key":"162035","doi-asserted-by":"crossref","unstructured":"[54] S.U. Rahman, H.S. Ali, B. Jafari, S. Zaib, A. Hameed, Y.M.S.A. Al-Kahraman, P. Langer, J. Iqbal. Structure-based virtual screening of dipeptidyl peptidase 4 inhibitors and their in vitro analysis. Computational Biology and Chemistry 91 (2021) 107326. https:\/\/doi.org\/10.1016\/j.compbiolchem.2020.107326","DOI":"10.1016\/j.compbiolchem.2020.107326"},{"key":"162036","doi-asserted-by":"crossref","unstructured":"[55] D. Istrate, L. Crisan. Dipeptidyl Peptidase 4 Inhibitors in Type 2 Diabetes Mellitus Management: Pharmacophore Virtual Screening, Molecular Docking, Pharmacokinetic Evaluations, and Conceptual DFT Analysis. Processes 11 (2023) 3100. https:\/\/doi.org\/10.3390\/pr11113100","DOI":"10.3390\/pr11113100"},{"key":"162037","doi-asserted-by":"crossref","unstructured":"[56] S.D. Ranade, S.G. Alegaon, N.A. Khatib, S. Gharge, R.S. Kavalapure. Quinoline-based Schiff bases as possible antidiabetic agents: ligand-based pharmacophore modeling, 3D QSAR, docking, and molecular dynamics simulations study. RSC Medicinal Chemistry 15 (2024) 3162-3179. http:\/\/dx.doi.org\/10.1039\/D4MD00344F","DOI":"10.1039\/D4MD00344F"},{"key":"162038","doi-asserted-by":"crossref","unstructured":"[57] D. Giordano, C. Biancaniello, M.A. Argenio, A. Facchiano. Drug Design by Pharmacophore and Virtual Screening Approach. Pharmaceuticals 15 (2022) 646. https:\/\/doi.org\/10.3390\/ph15050646","DOI":"10.3390\/ph15050646"},{"key":"162039","doi-asserted-by":"crossref","unstructured":"[58] S.A. Hollingsworth, R.O. Dror. Review Molecular Dynamics Simulation for All. Neuron 99 (2018) 1129-1143. https:\/\/doi.org\/10.1016\/j.neuron.2018.08.011","DOI":"10.1016\/j.neuron.2018.08.011"},{"key":"162040","doi-asserted-by":"crossref","unstructured":"[59] A.F. Maulana, S. Sriwidodo, Y. Rukayadi, I.P. Maksum. In Silico Study of Mangostin Compounds and Its Derivatives as Inhibitors of \u03b1-Glucosidase Enzymes for Anti-Diabetic Studies. Biology 11 (2022) 1837. https:\/\/doi.org\/10.3390\/biology11121837","DOI":"10.3390\/biology11121837"},{"key":"162041","doi-asserted-by":"crossref","unstructured":"[60] A. Smith, X. Dong, V. Raghavan. An Overview of Molecular Dynamics Simulation for Food Products and Processes. Processes 10 (2022) 119. https:\/\/doi.org\/10.3390\/pr10010119","DOI":"10.3390\/pr10010119"},{"key":"162042","doi-asserted-by":"crossref","unstructured":"[61] C.A. Lipinski, F. Lombardo, B.W. Dominy, P.J. Feeney. Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings. Advanced Drug Delivery Reviews 64 (2012) 4-17. https:\/\/doi.org\/10.1016\/j.addr.2012.09.019","DOI":"10.1016\/j.addr.2012.09.019"},{"key":"162043","doi-asserted-by":"crossref","unstructured":"[62] O.O. Olaokun, M.S. Zubair. Antidiabetic Activity, Molecular Docking, and ADMET Properties of Compounds Isolated from Bioactive Ethyl Acetate Fraction of Ficus lutea Leaf Extract. Molecules 28 (2023) 7717. https:\/\/doi.org\/10.3390\/molecules28237717","DOI":"10.3390\/molecules28237717"},{"key":"162044","doi-asserted-by":"crossref","unstructured":"[63] L. Fu, S. Shi, J. Yi, N. Wang, Y. He, Z. Wu, J. Peng, Y. Deng, W. Wang, C. Wu, A. Lyu, X. Zeng, W. Zhao, T. Hou, D. Cao. ADMETlab 3.0: an updated comprehensive online ADMET prediction platform enhanced with broader coverage, improved performance, API functionality and decision support. Nucleic Acids Research 52 (2024) W422-W431. https:\/\/doi.org\/10.1093\/nar\/gkae236","DOI":"10.1093\/nar\/gkae236"},{"key":"162045","doi-asserted-by":"crossref","unstructured":"[64] N. Flores-Holgu\u00edn, J. Frau, D. Glossman-Mitnik. Computational Pharmacokinetics Report, ADMET Study and Conceptual DFT-Based Estimation of the Chemical Reactivity Properties of Marine Cyclopeptides. ChemistryOpen 10 (2021) 1142-1149. https:\/\/doi.org\/10.1002\/open.202100178","DOI":"10.1002\/open.202100178"},{"key":"162046","doi-asserted-by":"crossref","unstructured":"[65] M. Nicolov, M. Cocora, V. Buda, C. Danciu, A.O. Duse, C. Watz, F. Borcan. Hydrosoluble and liposoluble vitamins: New perspectives through admet analysis. Medicina (Lithuania) 57 (2021) 1204. https:\/\/doi.org\/10.3390\/medicina57111204","DOI":"10.3390\/medicina57111204"},{"key":"162047","doi-asserted-by":"crossref","unstructured":"[66] D.F. Veber, S.R. Johnson, H. Cheng, B.R. Smith, K.D. Kopple. Molecular Properties That Influence the Oral Bioavailability of Drug Candidates. Journal of Medicinal Chemistry 45 (2002) 2615-2623. https:\/\/doi.org\/10.1021\/jm020017n","DOI":"10.1021\/jm020017n"},{"key":"162048","doi-asserted-by":"crossref","unstructured":"[67] W.J. Egan. Predicting ADME properties in drug discovery, Cambridge University Press, New York, USA, 2010. https:\/\/doi.org\/10.1017\/CBO9780511730412.013","DOI":"10.1017\/CBO9780511730412.013"},{"key":"162049","doi-asserted-by":"crossref","unstructured":"[68] P. V Zadorozhnii, V. V Kiselev, A. V Kharchenko. In Silico ADME Profiling of Salubrinal and Its Analogues. Future Pharmacology 2 (2022) 160-197. https:\/\/doi.org\/10.3390\/futurepharmacol2020013","DOI":"10.3390\/futurepharmacol2020013"},{"key":"162050","doi-asserted-by":"crossref","unstructured":"[69] D.E.V. Pires, T.L. Blundell, D.B. Ascher. pkCSM: Predicting small-molecule pharmacokinetic and toxicity properties using graph-based signatures. Journal of Medicinal Chemistry 58 (2015) 4066-4072. https:\/\/doi.org\/10.1021\/acs.jmedchem.5b00104","DOI":"10.1021\/acs.jmedchem.5b00104"},{"key":"162051","doi-asserted-by":"crossref","unstructured":"[70] A.-K. Sohlenius-Sternbeck, Y. Terelius. Evaluation of ADMET Predictor in Early Discovery Drug Metabolism and Pharmacokinetics Project Work. Drug Metabolism and Disposition 50 (2022) 95-104. https:\/\/doi.org\/10.1124\/dmd.121.000552","DOI":"10.1124\/dmd.121.000552"},{"key":"162052","doi-asserted-by":"crossref","unstructured":"[71] L. Wang, J. Chen, W. Chen, Z. Ruan, H. Lou, D. Yang, B. Jiang. In silico prediction of bioequivalence of atorvastatin tablets based on GastroPlusTM software. BMC Pharmacology and Toxicology 24 (2023) 69. https:\/\/doi.org\/10.1186\/s40360-023-00689-4","DOI":"10.1186\/s40360-023-00689-4"},{"key":"162053","doi-asserted-by":"crossref","unstructured":"[72] D. Naga, N. Parrott, G.F. Ecker. Evaluation of the Success of High-Throughput Physiologically Based Pharmacokinetic (HT-PBPK) Modeling Predictions to Inform Early Drug Discovery. Molecular Pharmaceutics 19 (2022) 2203-2216. https:\/\/doi.org\/10.1021\/acs.molpharmaceut.2c00040","DOI":"10.1021\/acs.molpharmaceut.2c00040"},{"key":"162054","doi-asserted-by":"crossref","unstructured":"[73] A. Daina, O. Michielin, V. Zoete. SwissADME : a free web tool to evaluate pharmacokinetics , drug- likeness and medicinal chemistry friendliness of small molecules. Nature Publishing Group 7 (2017) 42717. https:\/\/doi.org\/10.1038\/srep42717","DOI":"10.1038\/srep42717"},{"key":"162055","doi-asserted-by":"crossref","unstructured":"[74] B. Bakchi, A. Dileep, E. Sreecharan, V. Bala, J. Ganesh, M. Niharika, S. Maharshi, S. Babu, D. Kumar, R.R. Bhandare, A.B. Shaik. An overview on applications of SwissADME web tool in the design and development of anticancer , antitubercular and antimicrobial agents : A medicinal chemist \u2019 s perspective. Journal of Molecular Structure 1259 (2022) 132712. https:\/\/doi.org\/10.1016\/j.molstruc.2022.132712","DOI":"10.1016\/j.molstruc.2022.132712"},{"key":"162056","doi-asserted-by":"crossref","unstructured":"[75] J. Saczewski, L. Popenda, J. Fedorowicz. In Silico SwissADME Analysis of Antibacterial NHC - Silver Acetates and Halides Complexes. Applied Sciences 14 (2024) 8865. https:\/\/doi.org\/10.3390\/app14198865","DOI":"10.3390\/app14198865"},{"key":"162057","doi-asserted-by":"crossref","unstructured":"[76] K.M. Al Azzam. SwissADME and pkCSM Webservers Predictors : an integrated Online Platform for Accurate and Comprehensive Predictions for In Silico ADME \/ T Properties of Artemisinin and its Derivatives. Complex Use of Mineral Resources 325 (2023) 14-21. https:\/\/doi.org\/10.31643\/2023\/6445.13","DOI":"10.31643\/2023\/6445.13"},{"key":"162058","doi-asserted-by":"crossref","unstructured":"[77] Y. Gu, Z. Yu, Y. Wang, L. Chen, C. Lou, C. Yang, W. Li, G. Liu, Y. Tang. admetSAR3.0 : a comprehensive platform for exploration , prediction and optimization of chemical ADMET properties. Nucleic Acids Research 52 (2024) 432-438. https:\/\/doi.org\/10.1093\/nar\/gkae298","DOI":"10.1093\/nar\/gkae298"},{"key":"162059","doi-asserted-by":"crossref","unstructured":"[78] Y. Gu, C. Lou, Y. Tang. admetSAR-A valuable tool for assisting safety evaluation. in: QSAR Saf. Eval. Risk Assess., Academic Press, 2023, 187-202. https:\/\/doi.org\/10.1016\/B978-0-443-15339-6.00004-7","DOI":"10.1016\/B978-0-443-15339-6.00004-7"},{"key":"162060","doi-asserted-by":"crossref","unstructured":"[79] R.M. Romero, M.B. Bolger, N. Morningstar-kywi, I.S. Haworth. Teaching of Biopharmaceutics in a Drug Design Course: Use of GastroPlus as Educational Software. Journal of Chemical Education 97 (2020). https:\/\/doi.org\/10.1021\/acs.jchemed.0c00401","DOI":"10.1021\/acs.jchemed.0c00401"},{"key":"162061","doi-asserted-by":"crossref","unstructured":"[80] J. Dulsat, B. Lopez-Nieto, R. Estrada-Tejedor, J.I. Borrel. Evaluation of Free Online ADMET Tools for Academic or Small Biotech Environments. Molecules 28 (2023) 776. https:\/\/doi.org\/10.3390\/molecules28020776","DOI":"10.3390\/molecules28020776"},{"key":"162062","doi-asserted-by":"crossref","unstructured":"[81] K. Jurowski, A. Kro\u00b4sniak. ADME profile of AP-238 - opioid designer drug (CAS: 140924-11-4): first application of multi-in silico approach methodology for comprehensive prediction of ADME profile (absorption, distribution, metabolism and excretion) important for clinical toxicology. Chemico-Biological Interactions 415 (2025) 111493. https:\/\/doi.org\/10.1016\/j.cbi.2025.111493","DOI":"10.1016\/j.cbi.2025.111493"},{"key":"162063","doi-asserted-by":"crossref","unstructured":"[82] S. Jadhav, G. Vaidya, A. Vora. A review on computational tools for antidiabetic herbs research. Discover Chemistry 2 (2025) 80. https:\/\/doi.org\/10.1007\/s44371-025-00135-w","DOI":"10.1007\/s44371-025-00135-w"},{"key":"162064","doi-asserted-by":"crossref","unstructured":"[83] S. Kwon, H. Bae, J. Jo, S. Yoon. Comprehensive ensemble in QSAR prediction for drug discovery. BMC Bioinformatics 20 (2019) 521. https:\/\/doi.org\/10.1186\/s12859-019-3135-4","DOI":"10.1186\/s12859-019-3135-4"},{"key":"162065","unstructured":"[84] A. Mauri, V. Consonni, M. Pavan, R. Todeschini, M. Chemometrics. Dragon software: An easy approach to molecular descriptor calculations. Match 56 (2006) 237-248. https:\/\/match.pmf.kg.ac.rs\/electronic_versions\/Match56\/n2\/match56n2_237-248.pdf"},{"key":"162066","doi-asserted-by":"crossref","unstructured":"[85] C.. Yap. PaDEL\u2010descriptor: An open source software to calculate molecular descriptors and fingerprints. Journal of Computational Chemistry 37 (2010) 1466-1474. https:\/\/doi.org\/10.1002\/jcc.21707","DOI":"10.1002\/jcc.21707"},{"key":"162067","doi-asserted-by":"crossref","unstructured":"[86] A. Tropsha. Best Practices for QSAR Model Development , Validation , and Exploitation. Molecular Informatics 29 (2010) 476-488. https:\/\/doi.org\/10.1002\/minf.201000061","DOI":"10.1002\/minf.201000061"},{"key":"162068","doi-asserted-by":"crossref","unstructured":"[87] B. Sokouti, M.H. Mivehroud. 6D \u2011 QSAR for predicting biological activity of human aldose reductase inhibitors using quasar receptor surface modeling. BMC Chemistry 17 (2023) 63. https:\/\/doi.org\/10.1186\/s13065-023-00970-x","DOI":"10.1186\/s13065-023-00970-x"},{"key":"162069","doi-asserted-by":"crossref","unstructured":"[88] R. Veerasamy. QSAR\u2014An Important In-Silico Tool in Drug Design and Discovery. in: R. Srinivas, R. Kumar, M. Dutta (Eds.), Adv. Comput. Model. Simul., Springer Nature Singapore, Singapore, 2022: pp. 191-208. https:\/\/doi.org\/10.1007\/978-981-16-7857-8_16","DOI":"10.1007\/978-981-16-7857-8_16"},{"key":"162070","doi-asserted-by":"crossref","unstructured":"[89] Y. Wang, J. Li, X. Shi, Z. Wang, G. Hao, G. Yang. Web \u2011 Based Quantitative Structure - Activity Relation\u00acship. Topics in Current Chemistry 379 (2021) 37. https:\/\/doi.org\/10.1007\/s41061-021-00349-3","DOI":"10.1007\/s41061-021-00349-3"},{"key":"162071","doi-asserted-by":"crossref","unstructured":"[90] Y. Wang, F. Wang, X. Shi, C. Jia, F. Wu, G. Hao, G. Yang. Cloud 3D-QSAR : a web tool for the development of quantitative structure - activity relationship models in drug discovery. Briefings in Bioinformatics 22 (2021) bbaa276. https:\/\/doi.org\/10.1093\/bib\/bbaa276","DOI":"10.1093\/bib\/bbaa276"},{"key":"162072","doi-asserted-by":"crossref","unstructured":"[91] R. Perkins, H. Fang, W. Tong, W.J. Welsh. Quantitative structure\u2010activity relationship methods: Perspectives on drug discovery and toxicology. Environmental Toxicology and Chemistry 22 (2003) 1666-1679. https:\/\/doi.org\/10.1897\/01-171","DOI":"10.1897\/01-171"},{"key":"162073","doi-asserted-by":"crossref","unstructured":"[92] V. Siramshetty, J. Williams, \u00d0. Nguy, J. Neyra, N. Southall, E. Math\u00e9, X. Xu, P. Shah. Validating ADME QSAR Models Using Marketed Drugs. SLAS Discovery: Advancing the Science of Drug Discovery 26 (2021) 1326-1336. https:\/\/doi.org\/10.1177\/24725552211017520","DOI":"10.1177\/24725552211017520"},{"key":"162074","doi-asserted-by":"crossref","unstructured":"[93] N.L. Kruhlak, S.S. Choi, J.F. Contrera, J.L. Weaver, J.M. Willard, K.L. Hastings, L.F. Sancilio. Development of a Phospholipidosis Database and Predictive Quantitative Structure-Activity Relationship ( QSAR ) Models. Toxicology Mechanisms and Methods 18 (2008) 217-227. https:\/\/doi.org\/10.1080\/15376510701857262","DOI":"10.1080\/15376510701857262"},{"key":"162075","doi-asserted-by":"crossref","unstructured":"[94] A. Abuhammad, M.O. Taha, A. Abuhammad. QSAR studies in the discovery of novel type-II diabetic therapies QSAR studies in the discovery of novel type-II diabetic therapies. Expert Opinion on Drug Discovery 11 (2016) 197-214. https:\/\/doi.org\/10.1517\/17460441.2016.1118046","DOI":"10.1517\/17460441.2016.1118046"},{"key":"162076","doi-asserted-by":"crossref","unstructured":"[95] A.I. Odugbemi, C. Nyirenda, A. Christoffels, S.A. Egieyeh. Artificial intelligence in antidiabetic drug discovery : The advances in QSAR and the prediction of \u03b1 -glucosidase inhibitors. Computational and Structural Biotechnology Journal 23 (2024) 2964-2977. https:\/\/doi.org\/10.1016\/j.csbj.2024.07.003","DOI":"10.1016\/j.csbj.2024.07.003"},{"key":"162077","doi-asserted-by":"crossref","unstructured":"[96] M. Al Sharif, P. Alov, A. Diukendjieva, V. Vitcheva, R. Simeonova, I. Krasteva, A. Shkondrov, I. Tsakovska, I. Pajeva. Molecular determinants of PPAR\u03b3 partial agonism and related in silico\/in vivo studies of natural saponins as potential type 2 diabetes modulators. Food and Chemical Toxicology 112 (2018) 47-59. https:\/\/doi.org\/10.1016\/j.fct.2017.12.009","DOI":"10.1016\/j.fct.2017.12.009"},{"key":"162078","doi-asserted-by":"crossref","unstructured":"[97] W. Zheng, L. Qiu, R. Wang, X. Feng, Y. Han, Y. Zhu, D. Chen, Y. Liu, L. Jin, Y. Li. Selective targeting of PPAR\u03b3 by the natural product chelerythrine with a unique binding mode and improved antidiabetic potency. Scientific Reports 5 (2015) 12222. https:\/\/doi.org\/10.1038\/srep12222","DOI":"10.1038\/srep12222"},{"key":"162079","doi-asserted-by":"crossref","unstructured":"[98] T.N.H. Pham, T.H. Nguyen, N.M. Tam, T.Y. Vu, N.T. Pham, N.T. Huy, B.K. Mai, N.T. Tung, M.Q. Pham, V. V Vu, S.T. Ngo. Improving ligand-ranking of AutoDock Vina by changing the empirical parameters. Journal of Computational Chemistry 43 (2022) 160-169. https:\/\/doi.org\/10.1002\/jcc.26779","DOI":"10.1002\/jcc.26779"},{"key":"162080","doi-asserted-by":"crossref","unstructured":"[99] R. Buccheri, A. Rescifina. High-Throughput , High-Quality : Benchmarking GNINA and AutoDock Vina for Precision Virtual Screening Workflow \u2020. Molecules 30 (2025) 3361. https:\/\/doi.org\/10.3390\/ molecules30163361","DOI":"10.3390\/molecules30163361"},{"key":"162081","doi-asserted-by":"crossref","unstructured":"[100] R. Tiwari, K. Mahasenan, R. Pavlovicz, C. Li, W. Tjarks. Carborane Clusters in Computational Drug Design: A Comparative Docking Evaluation Using Autodock, Flexx, Glide and Surflex. Journal of Chemical Information and Modeling 49 (2010) 1581-1589. https:\/\/doi.org\/10.1021\/ci900031y","DOI":"10.1021\/ci900031y"},{"key":"162082","doi-asserted-by":"crossref","unstructured":"[101] D.A. Case, D.S. Cerutti, T.A. Darden, R.E. Duke, M. Ghazimirsaeed, G.M. Giambas, T.J. Giese, W.G. Andreas, J.A. Harris, K. Kasavajhala, T. Lee, Z. Li, C. Lin, J. Liu, Y. Miao, R. Salomon-ferrrer, J. Shen, R. Snyder, J. Swails, R.C. Walker, J. Wang, X. Wu, J. Zeng, T.E.C. Iii, D.R. Roe, A. Roitberg, C. Simmerling, D.M. York, M.C. Nagan, K.M. Merz. Recent Developments in Amber Biomolecular Simulations. Journal of Chemical Information and Modeling 65 (2025) 7835-7843. https:\/\/doi.org\/10.1021\/acs.jcim.5c01063","DOI":"10.1021\/acs.jcim.5c01063"},{"key":"162083","doi-asserted-by":"crossref","unstructured":"[102] A. Chakravorty, A. Hussain, L.F. Cervantes, T.T. Lai, C.L. Brooks III. Exploring the limits of the generalized CHARMM and AMBER force fields through predictions of hydration free energy of small molecules. Journal of Chemical Information and Modeling 64 (2025) 4089-4101. https:\/\/doi.org\/10.1021\/acs.jcim.4c00126","DOI":"10.1021\/acs.jcim.4c00126"},{"key":"162084","doi-asserted-by":"crossref","unstructured":"[103] J.C. Phillips, D.J. Hardy, J.D.C. Maia, J.E. Stone, J. V Ribeiro, R.C. Bernardi, R. Buch, G. Fiorin, J. H\u00e9nin, W. Jiang, R. Mcgreevy, B.K. Radak, R.D. Skeel, A. Singharoy. Scalable molecular dynamics on CPU and GPU architectures with NAMD. The Journal of Chemical Physics 153 (2020) 044130. https:\/\/doi.org\/10.1063\/5.0014475","DOI":"10.1063\/5.0014475"},{"key":"162085","doi-asserted-by":"crossref","unstructured":"[104] D. Yu, H. Li, Y. Liu, X. Yang, W. Yang, Y. Fu, Y. Zuo. Application of the molecular dynamics simulation GROMACS in food science. Food Research International 190 (2024) 114653. https:\/\/doi.org\/10.1016\/j.foodres.2024.114653","DOI":"10.1016\/j.foodres.2024.114653"},{"key":"162086","doi-asserted-by":"crossref","unstructured":"[105] S.L. Dixon, J. Duan, E. Smith, C.D. Von Bargen, M.P. Repasky. AutoQSAR: an automated machine learning tool for best-practice QSAR modeling. Future Medicinal Chemistry 8 (2016) 1825-1839. https:\/\/doi.org\/10.4155\/fmc-2016-0093","DOI":"10.4155\/fmc-2016-0093"},{"key":"162087","doi-asserted-by":"crossref","unstructured":"[106] L. Bordoli, T. Schwede. Automated Protein Structure Modeling with SWISS-MODEL Workspace and the Protein Model Portal. in: A.J.W. Orry, R. Abagyan (Eds.), Homol. Model. Methods Protoc., Humana Press, Totowa, NJ, 2012: pp. 107-136. https:\/\/doi.org\/10.1007\/978-1-61779-588-6_5","DOI":"10.1007\/978-1-61779-588-6_5"},{"key":"162088","doi-asserted-by":"crossref","unstructured":"[107] J. Oliveira, M. Izadora, O. Cardoso, A. Teodoro. BioProtIS : Streamlining protein-ligand interaction pipeline for analysis in genomic and transcriptomic exploration. Journal of Molecular Graphics and Modelling 128 (2024) 108721. https:\/\/doi.org\/10.1016\/j.jmgm.2024.108721","DOI":"10.1016\/j.jmgm.2024.108721"},{"key":"162089","doi-asserted-by":"crossref","unstructured":"[108] P. Antony, B. Baby, A. Jobe, R. Vijayan. Computational Modeling of the Interactions between DPP IV and Hemorphins. International Journal of Molecular Sciences 25 (2024) 3059. https:\/\/doi.org\/10.3390\/ijms25053059","DOI":"10.3390\/ijms25053059"},{"key":"162090","doi-asserted-by":"crossref","unstructured":"[109] C.A. Adinortey, G.B. Kwarko, R. Koranteng, D. Boison, I. Obuaba, M.D. Wilson, S.K. Kwofie. Molecular Structure-Based Screening of the Constituents of Calotropis procera Identifies Potential Inhibitors of Diabetes Mellitus Target Alpha Glucosidase. Current Issues in Molecular Biology 44 (2022) 963-987. https:\/\/doi.org\/10.3390\/cimb44020064","DOI":"10.3390\/cimb44020064"},{"key":"162091","doi-asserted-by":"crossref","unstructured":"[110] J.J. DiNicolantonio, J. Bhutani, J.H. O\u2019Keefe. Acarbose: safe and effective for lowering postprandial hyperglycaemia and improving cardiovascular outcomes. Open Heart 2 (2015) e000327. https:\/\/doi.org\/10.1136\/openhrt-2015-000327","DOI":"10.1136\/openhrt-2015-000327"},{"key":"162092","doi-asserted-by":"crossref","unstructured":"[111] Y.E. Lian, M. Wang, L. Ma, W. Yi, S. Liao, H. Gao, Z. Zhou. Identification of Novel PPAR\u03b3 Partial Agonists Based on Virtual Screening Strategy: In Silico and In Vitro Experimental Validation. Molecules 29 (2024) 4881. https:\/\/doi.org\/10.3390\/molecules29204881","DOI":"10.3390\/molecules29204881"},{"key":"162093","doi-asserted-by":"crossref","unstructured":"[112] K.A. Coughlan, R.J. Valentine, N.B. Ruderman, A.K. Saha. AMPK activation: a therapeutic target for type 2 diabetes? Diabetes, Metabolic Syndrome and Obesity: Targets and Therapy 7 (2014) 241-253. http:\/\/dx.doi.org\/10.2147\/DMSO.S43731","DOI":"10.2147\/DMSO.S43731"},{"key":"162094","doi-asserted-by":"crossref","unstructured":"[113] T. Huang, J. Sun, S. Zhou, J. Gao, Y. Liu. Identification of direct activator of adenosine monophosphate-activated protein kinase (AMPK) by structure-based virtual screening and molecular docking approach. International Journal of Molecular Sciences 18 (2017) 1408. https:\/\/doi.org\/10.3390\/ijms18071408","DOI":"10.3390\/ijms18071408"},{"key":"162095","doi-asserted-by":"crossref","unstructured":"[114] J. van Gerwen, A.S. Shun-Shion, D.J. Fazakerley. Insulin signalling and GLUT4 trafficking in insulin resis\u00actance. Biochemical Society Transactions 51 (2023) 1057-1069. https:\/\/doi.org\/10.1042\/BST20221066","DOI":"10.1042\/BST20221066"},{"key":"162096","doi-asserted-by":"crossref","unstructured":"[115] G. Song, Y. Huang, M. Xiong, Z. Yang, Q. Liu, J. Shen, P. Zhao, X. Yang. Aloperine Relieves Type 2 Diabetes Mellitus via Enhancing GLUT4 Expression and Translocation. Frontiers in Pharmacology 11 (2021) 561956. https:\/\/doi.org\/10.3389\/fphar.2020.561956","DOI":"10.3389\/fphar.2020.561956"},{"key":"162097","doi-asserted-by":"crossref","unstructured":"[116] P. Sharma, S. Singh, N. Sharma, D. Singla, K. Guarve, A.S. Grewal. Targeting human Glucokinase for the treatment of type 2 diabetes: an overview of allosteric Glucokinase activators. Journal of Dia-betes and Metabolic Disorders 21 (2022) 1129-1137. https:\/\/doi.org\/10.1007\/s40200-022-01019-x","DOI":"10.1007\/s40200-022-01019-x"},{"key":"162098","doi-asserted-by":"crossref","unstructured":"[117] Q. Min, X. Cai, W. Sun, F. Gao, Z. Li, Q. Zhang, L.S. Wan, H. Li, J. Chen. Identification of mangiferin as a potential Glucokinase activator by structure-based virtual ligand screening. Scientific Reports 7 (2017) 44681. https:\/\/doi.org\/10.1038\/srep44681","DOI":"10.1038\/srep44681"},{"key":"162099","doi-asserted-by":"crossref","unstructured":"[118] Z. Yang, F. Wu, Y. He, Q. Zhang, Y. Zhang, G. Zhou, H. Yang, P. Zhou. A novel PTP1B inhibitor extracted from: Ganoderma lucidum ameliorates insulin resistance by regulating IRS1-GLUT4 cascades in the insulin signaling pathway. Food & Function 9 (2018) 397-406. https:\/\/doi.org\/10.1039\/C7FO01489A","DOI":"10.1039\/C7FO01489A"},{"key":"162100","doi-asserted-by":"crossref","unstructured":"[119] C. Tanaka, N. Harada, Y. Teraoka, H. Urushizaki, Y. Shinmori, T. Onishi, Y. Yotsumoto, Y. Ito, T. Kitakaze, T. Inui, Y. Murata, H. Inui, R. Yamaji. Mogrol stimulates G-protein-coupled bile acid receptor 1 (GPBAR1\/TGR5) and insulin secretion from pancreatic \u03b2-cells and alleviates hyperglycemia in mice. Scientific Reports 14 (2024) 3244. https:\/\/doi.org\/10.1038\/s41598-024-53380-x","DOI":"10.1038\/s41598-024-53380-x"},{"key":"162101","doi-asserted-by":"crossref","unstructured":"[120] R. Salam, M. Bakker, M. Krut\u00e1kov\u00e1, A. \u0160tefela, P. P\u00e1vek, J. Duintjer Tebbens, J. Zitko. The discovery of a new nonbile acid modulator of Takeda G protein-coupled receptor 5: An integrated computational approach. Archiv Der Pharmazie 358 (2025) e2400423. https:\/\/doi.org\/10.1002\/ardp.202400423","DOI":"10.1002\/ardp.202400423"},{"key":"162102","doi-asserted-by":"crossref","unstructured":"[121] J. Xie, S. Wang, P. Ma, F. Ma, J. Li, W. Wang, F. Lu, H. Xiong, Y. Gu, S. Zhang, H. Xu, G. Yang, R.A. Lerner. Selection of Small Molecules that Bind to and Activate the Insulin Receptor from a DNA-Encoded Library of Natural Products. IScience 23 (2020) 101197. https:\/\/doi.org\/10.1016\/j.isci.2020.101197","DOI":"10.1016\/j.isci.2020.101197"},{"key":"162103","doi-asserted-by":"crossref","unstructured":"[122] H. Qian, J. Chen, Y. Pan, J. Chen. Molecular modeling studies of 11\u03b2-hydroxysteroid dehydrogenase type 1 inhibitors through receptor-based 3D-QSAR and molecular dynamics simulations. Molecules 21 (2016) 1222. https:\/\/doi.org\/10.3390\/molecules21091222","DOI":"10.3390\/molecules21091222"},{"key":"162104","doi-asserted-by":"crossref","unstructured":"[123] P.R. Murumkar, A.C. Shinde, M.K. Sharma, H. Yamaguchi, P.B. Miniyar, M.R. Yadav. Development of a credible 3D-QSAR CoMSIA model and docking studies for a series of triazoles and tetrazoles containing 11\u03b2-HSD1 inhibitors. SAR and QSAR in Environmental Research 27 (2016) 265-292. https:\/\/doi.org\/10.1080\/1062936X.2016.1167774","DOI":"10.1080\/1062936X.2016.1167774"},{"key":"162105","doi-asserted-by":"crossref","unstructured":"[124] S. Cosconati, S. Forli, A.L. Perryman, R. Harris, D.S. Goodsell, A.J. Olson. Virtual screening with AutoDock: Theory and practice. Expert Opinion on Drug Discovery 5 (2010) 597-607. https:\/\/doi.org\/10.1517\/17460441.2010.484460","DOI":"10.1517\/17460441.2010.484460"},{"key":"162106","doi-asserted-by":"crossref","unstructured":"[125] L. Pinzi, G. Rastelli. Molecular Docking: Shifting Paradigms in Drug Discovery. Molecular Sciences 20 (2019) 4331. https:\/\/doi.org\/10.3390\/ijms20184331","DOI":"10.3390\/ijms20184331"},{"key":"162107","doi-asserted-by":"crossref","unstructured":"[126] J. Fei, L. Zhou, T. Liu, X.Y. Tang. Pharmacophore modeling, virtual screening, and mo-lecular docking studies for discovery of novel Akt2 inhibitors. International Journal of Medical Sciences 10 (2013) 265-275. https:\/\/doi.org\/10.7150\/ijms.5344","DOI":"10.7150\/ijms.5344"},{"key":"162108","doi-asserted-by":"crossref","unstructured":"[127] C.-Y. Chang, Y. Ho, S.-J. Lin, H.-L. Liu. Discovery of Novel N-Glycoside and Non-Glycoside hSGLT2 Inhibitors for the Treatment of Type 2 Diabetes Mellitus. Journal of Diabetes Mellitus 9 (2019) 77-104. https:\/\/doi.org\/10.4236\/jdm.2019.93009","DOI":"10.4236\/jdm.2019.93009"},{"key":"162109","doi-asserted-by":"crossref","unstructured":"[128] I.M. Al-masri, M.K. Mohammad, M.O. Taha. Discovery of DPP IV inhibitors by pharmacophore modeling and QSAR analysis followed by in silico screening. ChemMedChem 3 (2008) 1763-1779. https:\/\/doi.org\/10.1002\/cmdc.200800213","DOI":"10.1002\/cmdc.200800213"},{"key":"162110","doi-asserted-by":"crossref","unstructured":"[129] T. Sakano, M.I. Mahmood, T. Yamashita, H. Fujitani. Molecular dynamics analysis to evaluate docking pose prediction. Biophysics and Physicobiology 13 (2016) 181-194. https:\/\/doi.org\/10.2142\/biophysico.13.0_181","DOI":"10.2142\/biophysico.13.0_181"},{"key":"162111","doi-asserted-by":"crossref","unstructured":"[130] M. Aminpour, C. Montemagno, J.A. Tuszynski. An overview of molecular modeling for drug discovery with specific illustrative examples of applications. Molecules 24 (2019) 1693. https:\/\/doi.org\/10.3390\/molecules24091693","DOI":"10.3390\/molecules24091693"},{"key":"162112","doi-asserted-by":"crossref","unstructured":"[131] E.O. Akintemi, K.K. Govender, T. Singh. Molecular Dynamics and Docking Investigation of Flavonol Aglycones against Sulfonylurea Receptor 1 (SUR1) for Anti-diabetic Drug Design. ChemistrySelect 9 (2024) e202302488. https:\/\/doi.org\/10.1002\/slct.202302488","DOI":"10.1002\/slct.202302488"},{"key":"162113","doi-asserted-by":"crossref","unstructured":"[132] A.H. G\u00f6ller, L. Kuhnke, F. Montanari, A. Bonin, S. Schneckener, A. Ter Laak, J. Wichard, M. Lobell, A. Hillisch. Bayer\u2019s in silico ADMET platform: a journey of machine learning over the past two decades. Drug Discovery Today 25 (2021) 1702-1709. https:\/\/doi.org\/10.1016\/j.drudis.2020.07.001","DOI":"10.1016\/j.drudis.2020.07.001"},{"key":"162114","doi-asserted-by":"crossref","unstructured":"[133] B.J. Neves, R.C. Braga, C.C. Melo-Filho, J.T. Moreira-Filho, E.N. Muratov, C.H. Andrade. QSAR-based virtual screening: Advances and applications in drug discovery. Frontiers in Pharmacology 9 (2018) 1275. https:\/\/doi.org\/10.3389\/fphar.2018.01275","DOI":"10.3389\/fphar.2018.01275"},{"key":"162115","doi-asserted-by":"crossref","unstructured":"[134] N.M. Abdelazeem, W.M. Aboulthana, A.S. Hassan, A.A. Almehizia, A.M. Naglah, H.M. Alkahtani. Synthesis, in silico ADMET prediction analysis, and pharmacological evaluation of sulfonamide derivatives tethered with pyrazole or pyridine as anti-diabetic and anti-Alzheimer\u2019s agents. Saudi Pharmaceutical Journal 32 (2024) 102025. https:\/\/doi.org\/10.1016\/j.jsps.2024.102025","DOI":"10.1016\/j.jsps.2024.102025"},{"key":"162116","doi-asserted-by":"crossref","unstructured":"[135] D. Hartanti, K. Budipramana. Traditional antidiabetic plants from indonesia. Ethnobotany Research and Applications 19 (2020) 1-24. http:\/\/dx.doi.org\/10.32859\/era.19.34.1-24","DOI":"10.32859\/era.19.34.1-24"},{"key":"162117","doi-asserted-by":"crossref","unstructured":"[136] I.R. Mohanty, C.S. Kumar, M. Borde. Antidiabetic activity of Commiphora mukul and Phyllanthus emblica and Computational analysis for the identification of active principles with dipeptidyl peptidase IV inhibitory activity. Indian Journal of Pharmacology 53 (2021) 384-387. https:\/\/doi.org\/10.4103\/ijp.IJP_69_19","DOI":"10.4103\/ijp.IJP_69_19"},{"key":"162118","doi-asserted-by":"crossref","unstructured":"[137] N. Swilam, M.A.M. Nawwar, R.A. Radwan, E.S. Mostafa. Antidiabetic Activity and In Silico Molecular Docking of Polyphenols from Ammannia baccifera L. subsp. Aegyptiaca (Willd.) Koehne Waste: Structure Elucidation of Undescribed Acylated Flavonol Diglucoside Noha. Plants 11 (2022) 452. https:\/\/doi.org\/10.3390\/plants11030452","DOI":"10.3390\/plants11030452"},{"key":"162119","doi-asserted-by":"crossref","unstructured":"[138] A.F. Maulana, I.P. Maksum, S. Sriwidodo, Y. Rukayadi. Proposed molecular mechanism of non-competitive inhibition using molecular dynamics simulations between \u03b1-glucosidase enzyme and mangostin compound as antidiabetic. Journal of Molecular Modeling 30 (2024) 136. https:\/\/doi.org\/10.1007\/s00894-024-05934-z","DOI":"10.1007\/s00894-024-05934-z"},{"key":"162120","doi-asserted-by":"crossref","unstructured":"[139] U. Subbiah, A. Athira, H. and Venkata Subbiah. Molecular docking and dynamics simulation of Orthosiphon stamineus against SGLT1 and SGLT2. Journal of Biomolecular Structure and Dynamics 41 (2023) 13663-13678. https:\/\/doi.org\/10.1080\/07391102.2023.2193984","DOI":"10.1080\/07391102.2023.2193984"},{"key":"162121","doi-asserted-by":"crossref","unstructured":"[140] D. Vijh, P. Gupta. GC-MS analysis, molecular docking, and pharmacokinetic studies on Dalbergia sissoo barks extracts for compounds with anti-diabetic potential. Scientific Reports 14 (2024) 24936. https:\/\/doi.org\/10.1038\/s41598-024-75570-3","DOI":"10.1038\/s41598-024-75570-3"},{"key":"162122","doi-asserted-by":"crossref","unstructured":"[141] B. Colin-Lozano, H. Torres-Gomez, S. Hidalgo-Figueroa, F. Ch\u00e1vez-Silva, S. Estrada-Soto, J.C. Almanza-P\u00e9rez, G. Navarrete-Vazquez. Synthesis, In Vitro, In Vivo and In Silico Antidiabetic Bioassays of 4-Nitro(thio)phenoxyisobutyric Acids Acting as Unexpected PPAR\u03b3 Modulators: An In Combo Study. Pharmaceuticals 15 (2022) 102. https:\/\/doi.org\/10.3390\/ph15010102","DOI":"10.3390\/ph15010102"},{"key":"162123","doi-asserted-by":"crossref","unstructured":"[142] M.A.R. Mia, Q.U. Ahmed, S. Ferdosh, A.B.M. Helaluddin, M.S. Awal, M.N. Sarian, M.Z.I. Sarker, Z.A. Zakaria. In Vitro, In Silico and Network Pharmacology Mechanistic Approach to Investigate the \u03b1-Glucosidase Inhibitors Identified by Q-ToF-LCMS from Phaleria macrocarpa Fruit Subcritical CO2 Extract. Metabolites 12 (2022) 1267. https:\/\/doi.org\/10.3390\/metabo12121267","DOI":"10.3390\/metabo12121267"},{"key":"162124","doi-asserted-by":"crossref","unstructured":"[143] K.O. Olufolabo, K. L\u00fcersen, S.A. Oguntimehin, V.A. Nchiozem-Ngnitedem, E.A. Agbebi, K.O. Faloye, D.K. Nyamboki, G. Rimbach, J.C. Matasyoh, B. Schmidt, J.O. Moody. In vitro and in silico studies reveal antidiabetic properties of arylbenzofurans from the root bark of Morus mesozygia Stapf. Frontiers in Pharmacology 15 (2024) 1338333. https:\/\/doi.org\/10.3389\/fphar.2024.1338333","DOI":"10.3389\/fphar.2024.1338333"},{"key":"162125","doi-asserted-by":"crossref","unstructured":"[144] U.V. Rekha, P. Rajagopal, R. Varadhachariyar, P.H. Priya, S.F. J.H, R. Govindan, C. Palanisamy, A.B. K.B, V.P. Veeraraghavan, S. Jayaraman. Molecular docking analysis of bioactive compounds from Cissampelos pareira with PPAR gamma. Bioinformation 18 (2022) 265-268. https:\/\/doi.org\/10.6026\/97320630018265","DOI":"10.6026\/97320630018265"},{"key":"162126","doi-asserted-by":"crossref","unstructured":"[145] M.O. Babalola, M.A. Ashiru, I.D. Boyenle, E.O. Atanda, K.I.Y. Oyedele, Abdul\u2011Quddus Dimeji, O. Awodele, N.A. Imaga. In vitro Analysis and Molecular Docking of Gas Chromatography-Mass Spectroscopy Fingerprints of Polyherbal Mixture Reveals Significant Antidiabetic Miture. Nigerian Journal of Experimental and Clinical Biosciences 10 (2022) 105-115. https:\/\/doi.org\/10.4103\/njecp.njecp_15_22","DOI":"10.4103\/njecp.njecp_15_22"},{"key":"162127","doi-asserted-by":"crossref","unstructured":"[146] I. Purwaningsih, I.P. Maksum, D. Sumiarsa, S. Sriwidodo. Isolation and Quantification of Palmatine from Fibraurea tinctoria Lour: In Vitro Antioxidant Activity and In Silico Antidiabetic Activity Evaluation. Drug Design, Development and Therapy 18 (2024) 3443-3459. https:\/\/doi.org\/10.2147\/DDDT.S454091","DOI":"10.2147\/DDDT.S454091"},{"key":"162128","doi-asserted-by":"crossref","unstructured":"[147] F. Noreen, S. Ullah, S.N. Mali, A. Khan, J. Hussain, A. Alshammari, N.A. Albekairi, R.D. Jawarkar, S. Yadav, A. Al-Harrasi, Z. Shafiq. Synthesis, in vitro, and in silico studies of 7-fluorochromone based thiosemicarbazones as \u03b1-glucosidase inhibitors. Scientific Reports 15 (2025) 9816. https:\/\/doi.org\/10.1038\/s41598-025-90156-3","DOI":"10.1038\/s41598-025-90156-3"},{"key":"162129","doi-asserted-by":"crossref","unstructured":"[148] S. Gul, F. Jan, A. Alam, A. Shakoor, A. Khan, A.F. AlAsmari, F. Alasmari, M. Khan, L. Bo. Synthesis, molecular docking and DFT analysis of novel bis-Schiff base derivatives with thiobarbituric acid for \u03b1-glucosidase inhibition assessment. Scientific Reports 14 (2024) 3419. https:\/\/doi.org\/10.1038\/s41598-024-54021-z","DOI":"10.1038\/s41598-024-54021-z"},{"key":"162130","doi-asserted-by":"crossref","unstructured":"[149] S.B. Zahra, S. Ullah, S.A. Halim, M. Waqas, N.U. Huda, A. Khan, A.Y. Binsaleh, A.F. El-kott, J. Hussain, A. Al-Harrasi, Z. Shafiq. Synthesis of novel coumarin-based thiosemicarbazones and their implications in diabetic management via in-vitro and in-silico approaches. Scientific Reports 13 (2023) 18014.https:\/\/doi.org\/10.1038\/s41598-023-44837-6","DOI":"10.1038\/s41598-023-44837-6"},{"key":"162131","doi-asserted-by":"crossref","unstructured":"[150] H. Khan, F. Jan, A. Shakoor, A. Khan, A.F. AlAsmari, F. Alasmari, S. Ullah, A. Al-Harrasi, M. Khan, S. Ali. Design, synthesis, molecular docking study, and \u03b1-glucosidase inhibitory evaluation of novel hydrazide-hydrazone derivatives of 3,4-dihydroxyphenylacetic acid. Scientific Reports 14 (2024) 11410. https:\/\/doi.org\/10.1038\/s41598-024-62034-x","DOI":"10.1038\/s41598-024-62034-x"},{"key":"162132","doi-asserted-by":"crossref","unstructured":"[151] H. Azizian, K. Pedrood, A. Moazzam, Y. Valizadeh, K. Khavaninzadeh, A. Zamani, M. Mohammadi-Khanaposhtani, S. Mojtabavi, M.A. Faramarzi, S. Hosseini, Y. Sarrafi, H. Adibi, B. Larijani, H. Rastegar, M. Mahdavi. Docking study, molecular dynamic, synthesis, anti-\u03b1-glucosidase assessment, and ADMET prediction of new benzimidazole-Schiff base derivatives. Scientific Reports 12 (2022) 14870. https:\/\/doi.org\/10.1038\/s41598-022-18896-0","DOI":"10.1038\/s41598-022-18896-0"},{"key":"162133","doi-asserted-by":"crossref","unstructured":"[152] G.S. Nongthombam, S.A. Ahmed, K. Saikia, S. Gogoi, J.C. Borah. Breaking boundaries in diabetic nephropathy treatment: design and synthesis of novel steroidal SGLT2 inhibitors. RSC Medicinal Chemistry 16 (2024) 296-311. https:\/\/doi.org\/10.1039\/D4MD00645C","DOI":"10.1039\/D4MD00645C"},{"key":"162134","doi-asserted-by":"crossref","unstructured":"[153] A. Imran, M.T. Shehzad, S.J.A. Shah, M. Laws, T. Al-Adhami, K.M. Rahman, I.A. Khan, Z. Shafiq, J. Iqbal. Development, Molecular Docking, and in Silico ADME Evaluation of Selective ALR2 Inhibitors for the Treatment of Diabetic Complications via Suppression of the Polyol Pathway. ACS Omega 7 (2022) 26425-26436. https:\/\/doi.org\/10.1021\/acsomega.2c02326","DOI":"10.1021\/acsomega.2c02326"},{"key":"162135","doi-asserted-by":"crossref","unstructured":"[154] A.S. Grewal, R. Kharb, D.N. Prasad, J.S. Dua, V. Lather. Design, synthesis and evaluation of novel 3,5-disubstituted benzamide derivatives as allosteric glucokinase activators. BMC Chemistry 13 (2019) 1-14. https:\/\/doi.org\/10.1186\/s13065-019-0532-8","DOI":"10.1186\/s13065-019-0532-8"},{"key":"162136","doi-asserted-by":"crossref","unstructured":"[155] S.K. Konidala, V. Kotra, R.C.S.R. Danduga, P.K. Kola. Coumarin-chalcone hybrids targeting insulin receptor: Design, synthesis, anti-diabetic activity, and molecular docking. Bioorganic Chemistry 104 (2020) 104207. https:\/\/doi.org\/10.1016\/j.bioorg.2020.104207","DOI":"10.1016\/j.bioorg.2020.104207"},{"key":"162137","doi-asserted-by":"crossref","unstructured":"[156] S. Kim, J. Chen, T. Cheng, A. Gindulyte, J. He, S. He, Q. Li, B.A. Shoemaker, P.A. Thiessen, B. Yu, L. Zaslavsky, J. Zhang, E.E. Bolton. PubChem 2023 update. Nucleic Acids Research 51 (2023) D1373-D1380. https:\/\/doi.org\/10.1093\/nar\/gkac956","DOI":"10.1093\/nar\/gkac956"},{"key":"162138","doi-asserted-by":"crossref","unstructured":"[157] A. Shahzad, W. Liu, S. Hussain, Y. Ni, K. Cui, Y. Sun, X. Liu, Q. Duan, J. Xia, J. Zhang, Z. Xu, B. Sai, Y. Zhu, Q. Zhang, Z. Yang. Integrated in vitro, in silico, and in vivo approaches to elucidate the antidiabetic mechanisms of Cicer arietinum and Hordeum vulgare extract and secondary metabolites. Scientific Reports 15 (2025) 6620. https:\/\/doi.org\/10.1038\/s41598-025-89642-5","DOI":"10.1038\/s41598-025-89642-5"},{"key":"162139","doi-asserted-by":"crossref","unstructured":"[158] W. Ndarawit, C.O. Ochieng, D. Angwenyi, J.N. Cruz, C.B.R. Santos, N.M. Kimani. Discovery of \u03b1-amylase and \u03b1-glucosidase dual inhibitors from NPASS database for management of Type 2 Diabetes Mellitus: A chemoinformatic approach. PloS One 19 (2024) e0313758. http:\/\/dx.doi.org\/10.1371\/journal.pone.0313758","DOI":"10.1371\/journal.pone.0313758"},{"key":"162140","doi-asserted-by":"crossref","unstructured":"[159] F. Zare, E. Ataollahi, P. Mardaneh, A. Sakhteman, V. Keshavarz, A. Solhjoo, L. Emami. A combination of virtual screening, molecular dynamics simulation, MM\/PBSA, ADMET, and DFT calculations to identify a potential DPP4 inhibitor. Scientific Reports 14 (2024) 7749. https:\/\/doi.org\/10.1038\/s41598-024-58485-x","DOI":"10.1038\/s41598-024-58485-x"},{"key":"162141","doi-asserted-by":"crossref","unstructured":"[160] A. Aldahish, P. Balaji, R. Vasudevan, G. Kandasamy, J.P. James, K. Prabahar. Elucidating the Potential Inhibitor against Type 2 Diabetes Mellitus Associated Gene of GLUT4. Journal of Personalized Medicine 13 (2023) 660. https:\/\/doi.org\/10.3390\/jpm13040660","DOI":"10.3390\/jpm13040660"},{"key":"162142","doi-asserted-by":"crossref","unstructured":"[161] M. Shahab, G. Zheng, F.M. Alshabrmi, M. Bourhia, G.F. Wondmie, A. Mohammad Salamatullah. Exploring potent aldose reductase inhibitors for anti-diabetic (anti-hyperglycemic) therapy: integrating structure-based drug design, and MMGBSA approaches. Frontiers in Molecular Biosciences 10 (2023) 1271569. https:\/\/doi.org\/10.3389\/fmolb.2023.1271569","DOI":"10.3389\/fmolb.2023.1271569"},{"key":"162143","doi-asserted-by":"crossref","unstructured":"[162] R.S.M. Rashid, S. Temurlu, A. Abourajab, P. Karsili, M. Dinleyici, B. Al-Khateeb, H. Icil. Drug Repurposing of FDA Compounds against \u03b1-Glucosidase for the Treatment of Type 2 Diabetes: Insights from Molecular Docking and Molecular Dynamics Simulations. Pharmaceuticals 16 (2023) 2750.","DOI":"10.3390\/ph16040555"},{"key":"162144","doi-asserted-by":"crossref","unstructured":"https:\/\/doi.org\/10.3390\/ph16040555","DOI":"10.3390\/ph16040555"},{"key":"162145","doi-asserted-by":"crossref","unstructured":"[163] O. Hermansyah, A. Bustamam, A. Yanuar. Virtual screening of dipeptidyl peptidase-4 inhibitors using quantitative structure-activity relationship-based artificial intelligence and molecular docking of hit compounds. Computational Biology and Chemistry 95 (2021) 107597. https:\/\/doi.org\/10.1016\/j.compbiolchem.2021.107597","DOI":"10.1016\/j.compbiolchem.2021.107597"},{"key":"162146","doi-asserted-by":"crossref","unstructured":"[164] M.E.L. Gakpey, S.A. Aidoo, T.A. Jumah, G. Hanson, S. Msipa, F.N. Mbaoji, O. Bukola, P.C. Tjale, M. Sangare, H. Tebourbi, O.I. Awe. Targeting aldose reductase using natural African compounds as promising agents for managing diabetic complications. Frontiers in Bioinformatics 5 (2025) 1499255. https:\/\/doi.org\/10.3389\/fbinf.2025.1499255","DOI":"10.3389\/fbinf.2025.1499255"},{"key":"162147","doi-asserted-by":"crossref","unstructured":"[165] J. Hao, Z. Qian, Z. Liu, G. Zhang, D. Wang, W. Han. The Antidiabetic Activities of Neocryptotanshinone: Screened by Molecular Docking and Related to the Modulation of PTP1B. Nutrients 14 (2022) 3031. https:\/\/doi.org\/10.3390\/nu14153031","DOI":"10.3390\/nu14153031"},{"key":"162148","unstructured":"[166] S.R.S. Prakash, S.M. Kamalnath, A.J. Antonisamy, S. Marimuthu, S. Malayandi. In Silico Molecular Docking of Phytochemicals for Type 2 Diabetes Mellitus Therapy: A Network Pharmacology Approach. International Journal of Molecular and Cellular Medicine 12 (2023) 372. https:\/\/doi.org\/10.22088\/IJMCM.BUMS.12.4.372"},{"key":"162149","doi-asserted-by":"crossref","unstructured":"[167] H.A. Bukhari, M. Afzal, F.A. Al-Abbasi, R.A. Sheikh, M.M. Alqurashi, A.S. Bawadood, S.I. Alzarea, A. Alamri, N. Sayyed, I. Kazmi. In vivo and computational investigation of butin against alloxan-induced diabetes via biochemical, histopathological, and molecular interactions. Scientific Reports 14 (2024) 20633. https:\/\/doi.org\/10.1038\/s41598-024-71577-y","DOI":"10.1038\/s41598-024-71577-y"},{"key":"162150","doi-asserted-by":"crossref","unstructured":"[168] R. Ratnawati, M. Aswad, J. Jumriani, A. Nurhidayah, M.R. Azmin, F. Filmaharani, A. Roosevelt, W. Hardiyanti, N.P. Latada, M. Mudjahid, F. Nainu. In Silico and In Vivo Investigation of the Anti-Hyperglycemic Effects of Caffeic Acid. ACS Omega 10 (2025) 14052-14062. https:\/\/doi.org\/10.1021\/acsomega.4c11062","DOI":"10.1021\/acsomega.4c11062"},{"key":"162151","doi-asserted-by":"crossref","unstructured":"[169] L. Vo Van, E.C. Pham, C.V. Nguyen, N.T.N. Duong, T. Vi Le Thi, T.N. Truong. In vitro and in vivo antidiabetic activity, isolation of flavonoids, and in silico molecular docking of stem extract of Merremia tridentata (L.). Biomedicine and Pharmacotherapy 146 (2022) 112611. https:\/\/doi.org\/10.1016\/j.biopha.2021.112611","DOI":"10.1016\/j.biopha.2021.112611"},{"key":"162152","doi-asserted-by":"crossref","unstructured":"[170] N. Nahar, M. Nazmul Hasan Zilani, P. Biswas, M. Morsaline Billah, S. Bibi, N.A. Albekairi, A. Alshammari, M. Nazmul Hasan. Profiling of secondary metabolite and evaluation of anti-diabetic potency of Crotalaria quinquefolia (L): In-vitro, in-vivo, and in-silico approaches. Saudi Pharmaceutical Journal 32 (2024) 101887. https:\/\/doi.org\/10.1016\/j.jsps.2023.101887","DOI":"10.1016\/j.jsps.2023.101887"},{"key":"162153","doi-asserted-by":"crossref","unstructured":"[171] J.A.A. Salazar, M. Valdes, A. Cruz, B. Moreno de Jes\u00fas, D. Pati\u00f1o Gonz\u00e1lez, I.M. Olivares Corichi, F. Tamay Cach, J.E. Mendieta Wejebe. In Silico and In Vivo Evaluation of Novel 2-Aminobenzothiazole Derivative Compounds as Antidiabetic Agents. International Journal of Molecular Sciences 26 (2025) 909. https:\/\/doi.org\/10.3390\/ijms26030909","DOI":"10.3390\/ijms26030909"},{"key":"162154","doi-asserted-by":"crossref","unstructured":"[172] B. Kirchweger, A. Wasilewicz, K. Fischhuber, A. Tahir, Y. Chen, E.H. Heiss, T. Langer, J. Kirchmair, J.M. Rollinger. In Silico and in Vitro Approach to Assess Direct Allosteric AMPK Activators from Nature#. Planta Medica 88 (2022) 794-804. https:\/\/doi.org\/10.1055\/a-1797-3030","DOI":"10.1055\/a-1797-3030"},{"key":"162155","doi-asserted-by":"crossref","unstructured":"[173] H. Lee, I. Youn, S.G. Noh, H.W. Kim, E. Song, S.J. Nam, H.Y. Chung, E.K. Seo. Identification of Bioactive Compounds from the Roots of Rehmannia glutinosa and Their In Silico and In Vitro AMPK Activation Potential. Molecules 29 (2024) 6009. https:\/\/doi.org\/10.3390\/molecules29246009","DOI":"10.3390\/molecules29246009"},{"key":"162156","doi-asserted-by":"crossref","unstructured":"[174] Y.O.N. Ditchou, P.B. Leutcha, D. Miaffo, H. Mamoudou, M.S. Ali, G.A. Amang \u00e0 Ngnoung, D. Soh, M. Agrawal, R. Darbawa, E. Zondegoumba Nkwengoua Tchouboun, A. Meli Lannang, X. Siwe Noundou. In vitro and in silico assessment of antidiabetic and antioxidant potencies of secondary metabolites from Gymnema sylvestre. Biomedicine and Pharmacotherapy 177 (2024) 117043. https:\/\/doi.org\/10.1016\/j.biopha.2024.117043","DOI":"10.1016\/j.biopha.2024.117043"},{"key":"162157","doi-asserted-by":"crossref","unstructured":"[175] J. Zhao, A.A. Najm, I. Mahmood, Z.Y. Ming, P.P. Dutta, W.H. Talib, D. Law, S. Fazry. In Vitro and In Silico Antidiabetic Efficacy of Solanum lasiocarpum Dunal Fruit Extract. PLoS ONE 20 (2025) e0312600. https:\/\/doi.org\/10.1371\/journal.pone.0312600","DOI":"10.1371\/journal.pone.0312600"},{"key":"162158","doi-asserted-by":"crossref","unstructured":"[176] D. Dhieb, D. Mustafa, M. Hassiba, M. Alasmar, M.H. Elsayed, A. Musa, M. Zirie, K. Bastaki. Harnessing Pharmacomultiomics for Precision Medicine in Diabetes: A Comprehensive Review. Biomedicines 13 (2025) 447. https:\/\/doi.org\/10.3390\/biomedicines13020447","DOI":"10.3390\/biomedicines13020447"},{"key":"162159","doi-asserted-by":"crossref","unstructured":"[177] A. Farnoud, A.J. Ohnmacht, M. Meinel, M.P. Menden. Can artificial intelligence accelerate preclinical drug discovery and precision medicine? Expert Opinion on Drug Discovery 17 (2022) 661-665. https:\/\/doi.org\/10.1080\/17460441.2022.2090540","DOI":"10.1080\/17460441.2022.2090540"},{"key":"162160","doi-asserted-by":"crossref","unstructured":"[178] H. Taherdoost, A. Ghofrani. AI\u2019s role in revolutionizing personalized medicine by reshaping pharmacogenomics and drug therapy. Intelligent Pharmacy 2 (2024) 643-650. https:\/\/doi.org\/10.1016\/j.ipha.2024.08.005","DOI":"10.1016\/j.ipha.2024.08.005"},{"key":"162161","doi-asserted-by":"crossref","unstructured":"[179] M.R. Shapiro, E.M. Tallon, M.E. Brown, A.L. Posgai, M.A. Clements, T.M. Brusko. Leveraging artificial intelligence and machine learning to accelerate discovery of disease-modifying therapies in type 1 diabetes. Diabetologia 68 (2024) 477-494. https:\/\/doi.org\/10.1007\/s00125-024-06339-6","DOI":"10.1007\/s00125-024-06339-6"},{"key":"162162","doi-asserted-by":"crossref","unstructured":"[180] G. Molla, M. Bitew. Revolutionizing Personalized Medicine: Synergy with Multi-Omics Data Generation, Main Hurdles, and Future Perspectives. 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