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<article xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns:ali="http://www.niso.org/schemas/ali/1.0/" article-type="research-article" dtd-version="1.2" xml:lang="en"><front><journal-meta><journal-id journal-id-type="publisher-id">Current Computer-Aided Drug Design</journal-id><journal-title-group><journal-title xml:lang="en">Current Computer-Aided Drug Design</journal-title><trans-title-group xml:lang="ru"><trans-title>Current Computer-Aided Drug Design</trans-title></trans-title-group></journal-title-group><issn publication-format="print">1573-4099</issn><issn publication-format="electronic">1875-6697</issn><publisher><publisher-name xml:lang="en">Bentham Science</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type="publisher-id">644076</article-id><article-id pub-id-type="doi">10.2174/1573409919666230515160502</article-id><article-categories><subj-group subj-group-type="toc-heading"><subject>Chemistry</subject></subj-group><subj-group subj-group-type="article-type"><subject>Research Article</subject></subj-group></article-categories><title-group><article-title xml:lang="en">Structure-based Virtual Screening and Molecular Dynamic Simulation Approach for the Identification of Terpenoids as Potential DPP-4 Inhibitors</article-title></title-group><contrib-group><contrib contrib-type="author"><name><surname>Pulikkottil</surname><given-names>Ajay</given-names></name><email>info@benthamscience.net</email><xref ref-type="aff" rid="aff1"/></contrib><contrib contrib-type="author"><name><surname>Kumar</surname><given-names>Amit</given-names></name><email>info@benthamscience.net</email><xref ref-type="aff" rid="aff1"/></contrib><contrib contrib-type="author"><name><surname>Jangid</surname><given-names>Kailash</given-names></name><email>info@benthamscience.net</email><xref ref-type="aff" rid="aff2"/></contrib><contrib contrib-type="author"><name><surname>Kumar</surname><given-names>Vinod</given-names></name><email>info@benthamscience.net</email><xref ref-type="aff" rid="aff2"/></contrib><contrib contrib-type="author"><name><surname>Jaitak</surname><given-names>Vikas</given-names></name><email>info@benthamscience.net</email><xref ref-type="aff" rid="aff1"/></contrib></contrib-group><aff id="aff1"><institution>Laboratory of Natural Product Chemistry, Department of Pharmaceutical Sciences and Natural Products, Central University of Punjab</institution></aff><aff id="aff2"><institution>Department of Chemistry, Central University of Punjab</institution></aff><pub-date date-type="pub" iso-8601-date="2024-04-01" publication-format="electronic"><day>01</day><month>04</month><year>2024</year></pub-date><volume>20</volume><issue>4</issue><issue-title xml:lang="ru"/><fpage>416</fpage><lpage>429</lpage><history><date date-type="received" iso-8601-date="2025-01-07"><day>07</day><month>01</month><year>2025</year></date></history><permissions><copyright-statement xml:lang="en">Copyright ©; 2024, Bentham Science Publishers</copyright-statement><copyright-year>2024</copyright-year><copyright-holder xml:lang="en">Bentham Science Publishers</copyright-holder><ali:free_to_read xmlns:ali="http://www.niso.org/schemas/ali/1.0/"/></permissions><self-uri xlink:href="https://journals.eco-vector.com/1573-4099/article/view/644076">https://journals.eco-vector.com/1573-4099/article/view/644076</self-uri><abstract xml:lang="en"><p id="idm46041443730128">Background:Diabetes mellitus is a metabolic disorder where insulin secretion is compromised, leading to hyperglycemia. DPP-4 is a viable and safer target for type 2 diabetes mellitus. Computational tools have proven to be an asset in the process of drug discovery.</p><p id="idm46041443734128">Objective:In the present study, tools like structure-based virtual screening, MM/GBSA, and pharmacokinetic parameters were used to identify natural terpenoids as potential DPP-4 inhibitors for treating diabetes mellitus.</p><p id="idm46041443738096">Methods:Structure-based virtual screening, a cumulative mode of elimination technique, was adopted, identifying the top five potent hit compounds depending on the docking score and nonbonding interactions.</p><p id="idm46041443743152">Results:According to the docking data, the most important contributors to complex stability are hydrogen bonding, hydrophobic interactions, and Pi-Pi stacking interactions. The dock scores ranged from -6.492 to -5.484 kcal/mol, indicating robust ligand-protein interactions. The pharmacokinetic characteristics of top-scoring hits (CNP0309455, CNP0196061, CNP0122006, CNP0 221869, CNP0297378) were also computed in this study, confirming their safe administration in the human body. Also, based on the synthetic accessibility score, all top-scored hits are easily synthesizable. Compound CNP0309455 was quite stable during molecular dynamic simulation studies.</p><p id="idm46041443752528">Conclusion:Virtual database screening yielded new leads for developing DPP-4 inhibitors. As a result, the findings of this study can be used to design and develop natural terpenoids as DPP-4 inhibitors for the medication of diabetes mellitus.</p></abstract><kwd-group xml:lang="en"><kwd>Diabetes mellitus</kwd><kwd>terpenoids</kwd><kwd>dipeptidyl peptidase-4</kwd><kwd>virtual screening</kwd><kwd>pharmacokinetic properties</kwd><kwd>molecular dynamic simulations.</kwd></kwd-group></article-meta></front><body></body><back><ref-list><ref id="B1"><label>1.</label><mixed-citation>Association, A.D. Diagnosis and classification of diabetes mellitus. Diabetes Care, 2009, 32(Suppl. 1), S62-S67. doi: 10.2337/dc09-S062 PMID: 19118289</mixed-citation></ref><ref id="B2"><label>2.</label><mixed-citation>Salehi, B.; Ata, A. Sharopov; Ramírez-Alarcón; Ruiz-Ortega; Abdulmajid Ayatollahi; Tsouh Fokou; Kobarfard; Amiruddin Zakaria; Iriti; Taheri; Martorell; Sureda; Setzer; Durazzo; Lucarini; Santini; Capasso; Ostrander; Atta-ur-Rahman; Choudhary, M.I.; Cho, W.C.; Sharifi-Rad, J.; Anil Kumar, V. Antidiabetic potential of medicinal plants and their active components. Biomolecules, 2019, 9(10), 551. doi: 10.3390/biom9100551 PMID: 31575072</mixed-citation></ref><ref id="B3"><label>3.</label><mixed-citation>Turdu, G.; Gao, H.; Jiang, Y.; Kabas, M. Plant dipeptidyl peptidase-IV inhibitors as antidiabetic agents: A brief review. Future Med. Chem., 2018, 10(10), 1229-1239. doi: 10.4155/fmc-2017-0235 PMID: 29749760</mixed-citation></ref><ref id="B4"><label>4.</label><mixed-citation>Genuth, S.M.; Palmer, J.P.; Nathan, D.M. Classification and diagnosis of diabetes. In: Diabetes in America, 3rd ed.; National Institute of Diabetes and Digestive and Kidney Diseases (US): Bethesda (MD); , 2021.</mixed-citation></ref><ref id="B5"><label>5.</label><mixed-citation>Shah, B.M.; Modi, P.; Trivedi, P. Pharmacophore- based virtual screening, 3D- QSAR, molecular docking approach for identification of potential dipeptidyl peptidase IV inhibitors. J. Biomol. Struct. Dyn., 2021, 39(6), 2021-2043. doi: 10.1080/07391102.2020.1750485 PMID: 32242496</mixed-citation></ref><ref id="B6"><label>6.</label><mixed-citation>Kato, E. Bioactive compounds in plant materials for the prevention of diabetesand obesity. Biosci. Biotechnol. Biochem., 2019, 83(6), 975-985. doi: 10.1080/09168451.2019.1580560 PMID: 30773997</mixed-citation></ref><ref id="B7"><label>7.</label><mixed-citation>Laha, S.; Paul, S. Gymnema sylvestre (Gurmar): A potent herb with anti-diabetic and antioxidant potential. Pharmacogn. J., 2019, 11(2), 201-206. doi: 10.5530/pj.2019.11.33</mixed-citation></ref><ref id="B8"><label>8.</label><mixed-citation>Williams, R.; Karuranga, S.; Malanda, B.; Saeedi, P.; Basit, A.; Besançon, S.; Bommer, C.; Esteghamati, A.; Ogurtsova, K.; Zhang, P.; Colagiuri, S. Global and regional estimates and projections of diabetes-related health expenditure: Results from the International Diabetes Federation Diabetes Atlas, 9th edition. Diabetes Res. Clin. Pract., 2020, 162, 108072. doi: 10.1016/j.diabres.2020.108072 PMID: 32061820</mixed-citation></ref><ref id="B9"><label>9.</label><mixed-citation>Lin, S.R.; Chang, C.H.; Tsai, M.J.; Cheng, H.; Chen, J.C.; Leong, M.K.; Weng, C.F. The perceptions of natural compounds against dipeptidyl peptidase 4 in diabetes: from in silico to in vivo. Ther. Adv. Chronic Dis., 2019, 10 doi: 10.1177/2040622319875305 PMID: 31555430</mixed-citation></ref><ref id="B10"><label>10.</label><mixed-citation>Pathak, R.; Bridgeman, M.B. Dipeptidyl peptidase-4 (DPP-4) inhibitors in the management of diabetes. P&amp;T, 2010, 35(9), 509-513. PMID: 20975810</mixed-citation></ref><ref id="B11"><label>11.</label><mixed-citation>Patel, B.D.; Bhadada, S.V.; Ghate, M.D. Design, synthesis and anti-diabetic activity of triazolotriazine derivatives as dipeptidyl peptidase-4 (DPP-4) inhibitors. Bioorg. Chem., 2017, 72, 345-358. doi: 10.1016/j.bioorg.2017.03.004 PMID: 28302310</mixed-citation></ref><ref id="B12"><label>12.</label><mixed-citation>Chen, S. The pharmacological effects of triterpenoids from Ganoderma lucidum and the regulation of its biosynthesis. Adv. Biol. Chem., 2020, 10(2), 55-65. doi: 10.4236/abc.2020.102005</mixed-citation></ref><ref id="B13"><label>13.</label><mixed-citation>Paul, R.K.; Nath, V.; Kumar, V. Structure based virtual screening of natural compounds and molecular dynamics simulation: Butirosin as Dipeptidyl peptidase (DPP-IV) inhibitor. Biocatal. Agric. Biotechnol., 2021, 35, 102042. doi: 10.1016/j.bcab.2021.102042</mixed-citation></ref><ref id="B14"><label>14.</label><mixed-citation>Yong-Lin, W.; Yan, Z.; Yan, T.; Yuan-Fang, K.; Yu-Long, HU.; Jie-Ming, LI.; Shao-Pei, W.; Chun-Hong, D.; Xiao-Fei, LI. Exploring of hypoglycemic mechanism of a Chinese Medicine Xiao-Ke-An based on target dipeptidyl peptidase-4: A molecular docking and molecular dynamics study., 2021.</mixed-citation></ref><ref id="B15"><label>15.</label><mixed-citation>Tahrani, A.A.; Bailey, C.J.; Del Prato, S.; Barnett, A.H. Management of type 2 diabetes: New and future developments in treatment. Lancet, 2011, 378(9786), 182-197. doi: 10.1016/S0140-6736(11)60207-9 PMID: 21705062</mixed-citation></ref><ref id="B16"><label>16.</label><mixed-citation>Upadhyay, S.; Dixit, M. Role of polyphenols and other phytochemicals on molecular signaling. Oxid. Med. Cell. Longev., 2015, 2015. doi: 10.1155/2015/504253</mixed-citation></ref><ref id="B17"><label>17.</label><mixed-citation>Pàmies, L.G. Identification of natural products as antidiabetic agents using computer-aided drug design methods; Universitat Rovira i Virgili, 2011.</mixed-citation></ref><ref id="B18"><label>18.</label><mixed-citation>Berger, J.P. SinhaRoy, R.; Pocai, A.; Kelly, T.M.; Scapin, G.; Gao, Y.D.; Pryor, K.A.D.; Wu, J.K.; Eiermann, G.J.; Xu, S.S.; Zhang, X.; Tatosian, D.A.; Weber, A.E.; Thornberry, N.A.; Carr, R.D. A comparative study of the binding properties, dipeptidyl peptidase-4 (DPP-4) inhibitory activity and glucose-lowering efficacy of the DPP-4 inhibitors alogliptin, linagliptin, saxagliptin, sitagliptin and vildagliptin in mice. Endocrinol. Diabetes Metab., 2018, 1(1), e00002. doi: 10.1002/edm2.2 PMID: 30815539</mixed-citation></ref><ref id="B19"><label>19.</label><mixed-citation>Schnapp, G.; Klein, T.; Hoevels, Y.; Bakker, R.A.; Nar, H. Comparative analysis of binding kinetics and thermodynamics of dipeptidyl peptidase-4 inhibitors and their relationship to structure. J. Med. Chem., 2016, 59(16), 7466-7477. doi: 10.1021/acs.jmedchem.6b00475 PMID: 27438064</mixed-citation></ref><ref id="B20"><label>20.</label><mixed-citation>Yoshida, T.; Akahoshi, F.; Sakashita, H.; Sonda, S.; Takeuchi, M.; Tanaka, Y.; Nabeno, M.; Kishida, H.; Miyaguchi, I.; Hayashi, Y. Fused bicyclic heteroarylpiperazine-substituted l-prolylthiazolidines as highly potent DPP-4 inhibitors lacking the electrophilic nitrile group. Bioorg. Med. Chem., 2012, 20(16), 5033-5041. doi: 10.1016/j.bmc.2012.06.033 PMID: 22824762</mixed-citation></ref><ref id="B21"><label>21.</label><mixed-citation>Kaur, K.K.; Allahbadia, G.; Singh, M. Monoterpenes-a class of terpenoid group of natural products as a source of natural antidiabetic agents in the futureA review. CPQ Nutrition, 2019, 3(4), 1-21.</mixed-citation></ref><ref id="B22"><label>22.</label><mixed-citation>Perveen, S.; Al-Taweel, A. Terpenes and terpenoids; BoDBooks on Demand, 2018. doi: 10.5772/intechopen.71175</mixed-citation></ref><ref id="B23"><label>23.</label><mixed-citation>Putta, S.; Sastry Yarla, N.; Kumar Kilari, E.; Surekha, C.; Aliev, G.; Basavaraju Divakara, M.; Sridhar Santosh, M.; Ramu, R.; Zameer, F.; Prasad, M.N. N.; Chintala, R.; Vijaya Rao, P.; Shiralgi, Y.; Lakkappa Dhananjaya, B.N Therapeutic potentials of triterpenes in diabetes and its associated complications. Curr. Top. Med. Chem., 2016, 16(23), 2532-2542. doi: 10.2174/1568026616666160414123343 PMID: 27086788</mixed-citation></ref><ref id="B24"><label>24.</label><mixed-citation>Tsiaka, T.; Kritsi, E.; Tsiantas, K.; Christodoulou, P.; Sinanoglou, V.J.; Zoumpoulakis, P. Design and development of novel nutraceuticals: Current trends and methodologies. Nutraceuticals, 2022, 2(2), 71-90. doi: 10.3390/nutraceuticals2020006</mixed-citation></ref><ref id="B25"><label>25.</label><mixed-citation>Sorokina, M.; Merseburger, P.; Rajan, K.; Yirik, M.A.; Steinbeck, C. COCONUT online: Collection of open natural products database. J. Cheminform., 2021, 13(1), 2. doi: 10.1186/s13321-020-00478-9 PMID: 33423696</mixed-citation></ref><ref id="B26"><label>26.</label><mixed-citation>Capecchi, A.; Reymond, J.L. Classifying natural products from plants, fungi or bacteria using the COCONUT database and machine learning. J. Cheminform., 2021, 13(1), 82. doi: 10.1186/s13321-021-00559-3 PMID: 34663470</mixed-citation></ref><ref id="B27"><label>27.</label><mixed-citation>Alshehri, B.; Vijayakumar, R.; Senthilkumar, S.; Ismail, A.; Abdelhadi, A.; Choudhary, R.K.; Albenasy, K.S.; Banawas, S.; Alaidarous, M.A.; Manikandan, P. Molecular target prediction and docking of anti-thrombosis compounds and its activation on tissue-plasminogen activator to treat stroke. J. King Saud Univ. Sci., 2022, 34(1), 101732. doi: 10.1016/j.jksus.2021.101732</mixed-citation></ref><ref id="B28"><label>28.</label><mixed-citation>Lu, C.; Wu, C.; Ghoreishi, D.; Chen, W.; Wang, L.; Damm, W.; Ross, G.A.; Dahlgren, M.K.; Russell, E.; Von Bargen, C.D.; Abel, R.; Friesner, R.A.; Harder, E.D. OPLS4: Improving force field accuracy on challenging regimes of chemical space. J. Chem. Theory Comput., 2021, 17(7), 4291-4300. doi: 10.1021/acs.jctc.1c00302 PMID: 34096718</mixed-citation></ref><ref id="B29"><label>29.</label><mixed-citation>Madhavi Sastry, G.; Adzhigirey, M.; Day, T.; Annabhimoju, R.; Sherman, W. Protein and ligand preparation: Parameters, protocols, and influence on virtual screening enrichments. J. Comput. Aided Mol. Des., 2013, 27(3), 221-234. doi: 10.1007/s10822-013-9644-8 PMID: 23579614</mixed-citation></ref><ref id="B30"><label>30.</label><mixed-citation>Teli, D.M.; Shah, M.B.; Chhabria, M.T. In silico screening of natural compounds as potential inhibitors of SARS-CoV-2 main protease and spike RBD: Targets for COVID-19. Front. Mol. Biosci., 2021, 7, 599079. doi: 10.3389/fmolb.2020.599079 PMID: 33542917</mixed-citation></ref><ref id="B31"><label>31.</label><mixed-citation>Rohane, S.H.; Makwana, A.G. In silico study for the prediction of multiple pharmacological activities of novel hydrazone derivatives. Indian J. Chem., 2019, 58, 387-402.</mixed-citation></ref><ref id="B32"><label>32.</label><mixed-citation>Greenwood, J.R.; Calkins, D.; Sullivan, A.P.; Shelley, J.C. Towards the comprehensive, rapid, and accurate prediction of the favorable tautomeric states of drug-like molecules in aqueous solution. J. Comput. Aided Mol. Des., 2010, 24(6-7), 591-604. doi: 10.1007/s10822-010-9349-1 PMID: 20354892</mixed-citation></ref><ref id="B33"><label>33.</label><mixed-citation>Shelley, J.C.; Cholleti, A.; Frye, L.L.; Greenwood, J.R.; Timlin, M.R.; Uchimaya, M. Epik: A software program for pK a prediction and protonation state generation for drug-like molecules. J. Comput. Aided Mol. Des., 2007, 21(12), 681-691. doi: 10.1007/s10822-007-9133-z PMID: 17899391</mixed-citation></ref><ref id="B34"><label>34.</label><mixed-citation>Marondedze, E.F.; Govender, K.K.; Govender, P.P. Ligand-based pharmacophore modelling and virtual screening for the identification of amyloid-beta diagnostic molecules. J. Mol. Graph. Model., 2020, 101, 107711. doi: 10.1016/j.jmgm.2020.107711 PMID: 32898834</mixed-citation></ref><ref id="B35"><label>35.</label><mixed-citation>Halgren, T.A.; Murphy, R.B.; Friesner, R.A.; Beard, H.S.; Frye, L.L.; Pollard, W.T.; Banks, J.L. Glide: A new approach for rapid, accurate docking and scoring. 2. Enrichment factors in database screening. J. Med. Chem., 2004, 47(7), 1750-1759. doi: 10.1021/jm030644s PMID: 15027866</mixed-citation></ref><ref id="B36"><label>36.</label><mixed-citation>Duan, J.; Dixon, S.L.; Lowrie, J.F.; Sherman, W. Analysis and comparison of 2D fingerprints: Insights into database screening performance using eight fingerprint methods. J. Mol. Graph. Model., 2010, 29(2), 157-170. doi: 10.1016/j.jmgm.2010.05.008 PMID: 20579912</mixed-citation></ref><ref id="B37"><label>37.</label><mixed-citation>Sastry, M.; Lowrie, J.F.; Dixon, S.L.; Sherman, W. Large-scale systematic analysis of 2D fingerprint methods and parameters to improve virtual screening enrichments. J. Chem. Inf. Model., 2010, 50(5), 771-784. doi: 10.1021/ci100062n PMID: 20450209</mixed-citation></ref><ref id="B38"><label>38.</label><mixed-citation>Shivakumar, D.; Williams, J.; Wu, Y.; Damm, W.; Shelley, J.; Sherman, W. Prediction of absolute solvation free energies using molecular dynamics free Energy Perturbation and the OPLS force field. J. Chem. Theory Comput., 2010, 6(5), 1509-1519. doi: 10.1021/ct900587b PMID: 26615687</mixed-citation></ref><ref id="B39"><label>39.</label><mixed-citation>Li, J.; Abel, R.; Zhu, K.; Cao, Y.; Zhao, S.; Friesner, R.A. The VSGB 2.0 model: A next generation energy model for high resolution protein structure modeling. Proteins, 2011, 79(10), 2794-2812. doi: 10.1002/prot.23106 PMID: 21905107</mixed-citation></ref><ref id="B40"><label>40.</label><mixed-citation>Croitoru, A.; Park, S.J.; Kumar, A.; Lee, J. Im, W.; MacKerell, A.D., Jr; Aleksandrov, A. Additive CHARMM36 force field for nonstandard amino acids. J. Chem. Theory Comput., 2021, 17(6), 3554-3570. doi: 10.1021/acs.jctc.1c00254 PMID: 34009984</mixed-citation></ref><ref id="B41"><label>41.</label><mixed-citation>Evans, D.J.; Holian, B.L. The nosehoover thermostat. J. Chem. Phys., 1985, 83(8), 4069-4074. doi: 10.1063/1.449071</mixed-citation></ref><ref id="B42"><label>42.</label><mixed-citation>Berendsen, H.J.C.; Postma, J.P.M.; van Gunsteren, W.F.; DiNola, A.; Haak, J.R. Molecular dynamics with coupling to an external bath. J. Chem. Phys., 1984, 81(8), 3684-3690. doi: 10.1063/1.448118</mixed-citation></ref><ref id="B43"><label>43.</label><mixed-citation>Lin, X.; Xu, Y.; Pan, X.; Xu, J.; Ding, Y.; Sun, X.; Song, X.; Ren, Y.; Shan, P.F. Global, regional, and national burden and trend of diabetes in 195 countries and territories: an analysis from 1990 to 2025. Sci. Rep., 2020, 10(1), 14790. doi: 10.1038/s41598-020-71908-9 PMID: 32901098</mixed-citation></ref><ref id="B44"><label>44.</label><mixed-citation>Chen, D.; Oezguen, N.; Urvil, P.; Ferguson, C.; Dann, S.M.; Savidge, T.C. Regulation of protein-ligand binding affinity by hydrogen bond pairing. Sci. Adv., 2016, 2(3), e1501240. doi: 10.1126/sciadv.1501240 PMID: 27051863</mixed-citation></ref><ref id="B45"><label>45.</label><mixed-citation>Kufareva, I.; Abagyan, R. Methods of Protein Structure Comparison. In: Homology Modeling: Methods and Protocols; Orry, A.J.W.; Abagyan, R., Eds.; Humana Press: Totowa, NJ, 2012; pp. 231-257.</mixed-citation></ref><ref id="B46"><label>46.</label><mixed-citation>Prashant, S.; Murthy, D.K. In silico evaluation of 2, 4-diaminoquinazoline derivatives as possible anticancer agents. Curr. Trends Biotechnol. Pharm., 2022, 16(1), 14-22.</mixed-citation></ref><ref id="B47"><label>47.</label><mixed-citation>Mendie, L.E.; Hemalatha, S. Molecular docking of phytochemicals targeting gfrs as therapeutic sites for cancer: An in silico study. Appl. Biochem. Biotechnol., 2022, 194(1), 215-231. doi: 10.1007/s12010-021-03791-7 PMID: 34988844</mixed-citation></ref><ref id="B48"><label>48.</label><mixed-citation>Baber, J.; Feher, M. Predicting synthetic accessibility: Application in drug discovery and development. Mini Rev. Med. Chem., 2004, 4(6), 681-692. doi: 10.2174/1389557043403765 PMID: 15279602</mixed-citation></ref><ref id="B49"><label>49.</label><mixed-citation>Karmakar, S.; Basak, H.K.; Paswan, U.; Pramanik, A.K.; Chatterjee, A. Designing of next-generation dihydropyridine-based calcium channel blockers: An in silico study. J. Appl. Pharm. Sci., 2022, 12(04), 127-135. doi: 10.7324/JAPS.2022.120414</mixed-citation></ref><ref id="B50"><label>50.</label><mixed-citation>Warr, W.A. A short review of chemical reaction database systems, computer-aided synthesis design, reaction prediction and synthetic feasibility. Mol. Inform., 2014, 33(6-7), 469-476. doi: 10.1002/minf.201400052 PMID: 27485985</mixed-citation></ref><ref id="B51"><label>51.</label><mixed-citation>Geetha, R.V.; Roy, A. Essential oil repellents-a short review. Int. J. Drug Dev. Res., 2014, 6, 20-27.</mixed-citation></ref><ref id="B52"><label>52.</label><mixed-citation>Peng, J.; Franzblau, S.G.; Zhang, F.; Hamann, M.T. Novel sesquiterpenes and a lactone from the Jamaican sponge Myrmekioderma styx. Tetrahedron Lett., 2002, 43(52), 9699-9702. doi: 10.1016/S0040-4039(02)02369-9</mixed-citation></ref><ref id="B53"><label>53.</label><mixed-citation>Serra, S. Enantioselective synthesis of the bisabolane sesquiterpene (+)-1-hydroxy-1,3,5-bisabolatrien-10-one and revision of its absolute configuration. Nat. Prod. Commun., 2012, 7(4), 1934578X1200700. doi: 10.1177/1934578X1200700409 PMID: 22574440</mixed-citation></ref><ref id="B54"><label>54.</label><mixed-citation>Vernin, G.; Lageot, C.; Gaydou, E.M.; Parkanyi, C. Analysis of the essential oil ofLippia graveolens HBK from El Salvador. Flavour Fragrance J., 2001, 16(3), 219-226. doi: 10.1002/ffj.984</mixed-citation></ref><ref id="B55"><label>55.</label><mixed-citation>Zdero, C.; Bohlmann, F.; Niemeyer, H.M. Sesquiterpene lactones from Perityle emoryi. Phytochemistry, 1990, 29(3), 891-894. doi: 10.1016/0031-9422(90)80040-N</mixed-citation></ref><ref id="B56"><label>56.</label><mixed-citation>Cox-Georgian, D.; Ramadoss, N.; Dona, C.; Basu, C. Therapeutic and medicinal uses of terpenes. J. Med. Plant Res., 2019, 333-359.</mixed-citation></ref><ref id="B57"><label>57.</label><mixed-citation>Brahmachari, G. Andrographolide: A Molecule of Antidiabetic Promise. In: Discovery and Development of Antidiabetic Agents from Natural Products; Brahmachari, G., Ed.; Elsevier, 2017; pp. 1-27. doi: 10.1016/B978-0-12-809450-1.00001-6</mixed-citation></ref><ref id="B58"><label>58.</label><mixed-citation>Salim, B.; Hocine, A.; Said, G. First study on anti-diabetic effect of rosemary and salvia by using molecular docking. J. Pharm. Res., 2017, 1-12.</mixed-citation></ref><ref id="B59"><label>59.</label><mixed-citation>Sette-de-Souza, P. H.; Souza, B. A. A.; Costa, M. J. F.; da Costa Araújo, F. A. Kuguacin: biological activities of triterpenoid from Momordica charantia-a scoping review. Adv. Trad. Med.,, 2021, 1-8.</mixed-citation></ref><ref id="B60"><label>60.</label><mixed-citation>Singla, R.; Singla, N.; Jaitak, V. Stevia rebaudiana targeting α-amylase: An in-vitro and in-silico mechanistic study. Nat. Prod. Res., 2019, 33(4), 548-552. doi: 10.1080/14786419.2017.1395433 PMID: 29072099</mixed-citation></ref><ref id="B61"><label>61.</label><mixed-citation>Matsabisa, M.G.; Chukwuma, C.I.; Chaudhary, S.K.; Kumar, C.S.; Baleni, R.; Javu, M.; Oyedemi, S.O. Dicoma anomala (Sond.) abates glycation and DPP-IV activity and modulates glucose utilization in Chang liver cells and 3T3-L1 adipocytes. S. Afr. J. Bot., 2020, 128, 182-188. doi: 10.1016/j.sajb.2019.09.013</mixed-citation></ref><ref id="B62"><label>62.</label><mixed-citation>Kato, E.; Kawakami, K.; Kawabata, J. Macrocarpal C isolated from Eucalyptus globulus inhibits dipeptidyl peptidase 4 in an aggregated form. J. Enzyme Inhib. Med. Chem., 2018, 33(1), 106-109. doi: 10.1080/14756366.2017.1396458 PMID: 29148282</mixed-citation></ref></ref-list></back></article>
