Patil, P. D., Mahajan, U. B., Patil, K. R., Chaudhari, S., Patil, C. R., Agrawal, Y. O., Ojha, S., & Goyal, S. N. (2017). Past and current perspective on new therapeutic targets for Type-II diabetes. Drug Design, Development and Therapy, 11, 1567–1583.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Hassanzadeh, V., Mehdinejad, M. H., & Ferrante, M., et al. (2016). Association between polychlorinated biphenyls in the serum and adipose tissue with type 2 diabetes mellitus: a systematic review and meta-analysis. Health Science, 5(9), 13–21.

Google Scholar 

Riyaphan, J., Pham, D. C., Leong, M. K., & Weng, C. F. (2021). In silico approaches to identify polyphenol compounds as α-glucosidase and α-amylase inhibitors against type-II diabetes. Biomolecules, 11(12), 1877.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Yamazaki, T., Mimura, I., Tanaka, T., & Nangaku, M. (2021). Treatment of diabetic kidney disease: Current and future. Diabetes & Metabolism Journal, 45(1), 11–26.

Article  Google Scholar 

Kaur, N., Kumar, V., Nayak, S. K., Wadhwa, P., Kaur, P., & Sahu, S. K., et al. (2021). Alpha-amylase as molecular target for treatment of diabetes mellitus: A comprehensive review. Chemical Biology & Drug Design., 98, 539–560.

Article  CAS  Google Scholar 

Janecek, S., Svensson, B., & MacGregor, E. A. (2014). Alpha-Amylase: An enzyme specificity found in various families of glycoside hydrolases. Cellular and Molecular Life Science, 71(7), 1149–1170.

Article  CAS  Google Scholar 

Nandi, S., & Saxena, M. (2020). Potential inhibitors of protein tyrosine phosphatase (PTP1B) enzyme: Promising target for type-II diabetes mellitus. Current Topics in Medicinal Chemistry, 20(29), 2692–2707.

Article  CAS  PubMed  Google Scholar 

Petersen, M. C., & Shulman, G. I. (2018). Mechanisms of insulin action and insulin resistance. Physiological Reviews, 98(4), 2133–2223.

Article  CAS  PubMed  PubMed Central  Google Scholar 

O’Brien, M. J., Karam, S. L., Wallia, A., Kang, R. H., Cooper, A. J., Lancki, N., Moran, M. R., Liss, D. T., Prospect, T. A., & Ackermann, R. T. (2018). Association of second-line Antidiabetic medications with cardiovascular events among insured adults with Type 2 diabetes. JAMA Network Open, 1, 186125.

Article  Google Scholar 

Patil, S. M., Martiz, R. M., Satish, A. M., Shbeer, A. M., Ageel, M., AI- Ghorbani, M., Ranganatha, 5th, L., Parameswaran, S., & Ramu, R. (2022). Discovery of novel coumarin derivatives as potential dual inhibitors against α-glucosidase and α-amylase for the management of post-prandial hyperglycemia via molecular modelling approaches. Molecules, 27(12), 3888.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Hostalek, U., Gwilt, M., & Hildemann, S. (2015). Therapeutic use of metformin in prediabetes and diabetes prevention. Drugs, 75(10), 1071–1094.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Afifi, A. F., Kamel, E. M., Khalil, A. A., Foaad, M. A., Fawziand, E. M., & Houseny, M. (2008). Purification and characterization of a-amylase from penicilliumolsonii under the effect of some antioxidant vitamins. Global Journal of Biotechnology and Biochemistry, 3(1), 14–12.

Google Scholar 

Rasouli, H., Hosseini-Ghazvini, S. M., Adibi, H., & Khodarahmi, R. (2017). Differential alpha-amylase/alpha-glucosidase inhibitory activities of plant-derived phenolic compounds: A virtual screening perspective for the treatment of obesity and diabetes. Food Funct, 8, 1942–1954.

Article  CAS  PubMed  Google Scholar 

Kar, A., Choudhary, B. K., & Bandyopadhyay, N. G. (2003). Comparative evaluation of hypoglycaemic activity of some Indian medicinal plants in alloxan diabetic rats. Journal of Ethnopharmacology, 84, 105–108.

Article  PubMed  Google Scholar 

Chiasson, J. L., Josse, R. G., Gomis, R., Hanefeld, M., Karasik, A., & Laakso, M. (2002). Acarbose for prevention of type 2 diabetes mellitus: the STOP-NIDDM randomised trial. Lancet, 3(9), 2072–2077.

Article  Google Scholar 

Khan, F., & Kumar, A. (2021). An integrative docking and simulation-based approach towards the development of epitope-based vaccine against enterotoxigenic Escherichia coli. Network Modeling and Analysis in Health Informatics Bioinform, 10(1), 11.

Article  Google Scholar 

Khan, F., Srivastava, V., & Kumar, A. (2018). Epitope based peptide prediction from proteome of enterotoxigenic E.coli. International Journal of Peptide Research Therapeutics, 24, 323–336.

Article  CAS  Google Scholar 

Bian, Y., & Xie, X. S. (2018). Computational fragment-based drug design: Current trends, strategies, and applications. AAPS Journal, 20(3), 59.

Article  PubMed  Google Scholar 

Chenafa, H., Fouzia, M., Ismail, D., Radja, A., Said, G., & Abdelhak, N. (2022). In silico design of enzyme α-amylase and α-glucosidase inhibitors using molecular docking, molecular dynamic, conceptual DFT investigation and pharmacophore modelling. Journal of Biomolecular Structure and Dynamics, 40(14), 6308–6329.

Article  CAS  PubMed  Google Scholar 

Ravindranth, P. A., Forli, S., Goodsell, D. S., Olson, A. J., & Sanner, M. F. (2015). AutoDockFR: Advances in protein-ligand docking with explicitly specified binding site flexibility. PLoS Computational Biology, 11(12), e1004586.

Article  Google Scholar 

Maurus, R., Begum, A., Williams, L. K., Fredriksen, J. R., Zhang, R., Withers, S. G., & Brayer, G. D. (2008). Alternative catalytic anions differentially modulate human alpha-amylase activity and specificity. Biochemistry, 47(11), 3332–3344.

Article  CAS  PubMed  Google Scholar 

Kemmish, H., Fasnacht, M., & Yan, L. (2017). Fully automated antibody structure prediction using BIOVIA tools: Validation study. PLoS One, 12(5), e0177923.

Article  PubMed  PubMed Central  Google Scholar 

Dundas, J., Ouyang, Z., Tseng, J., Binkowski, A., Turpaz, Y., & Liang, J. (2006). CASTp: Computed atlas of surface topography of proteins with structural and topographical mapping of functionally annotated residues. Nucleic Acids Research, 34, W116–W118.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Lipinski, C. A., Lombardo, F., Dominy, B. W., & Feeney, P. J. (1997). Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings. Advanced Drug Delivery Reviews, 23(1-3), 3–25.

Article  CAS  Google Scholar 

Yamaguchi, M., Saji, T., Mita, S., Kulmatycki, K., He, Y. L., Furihata, K., & Sekiguchi, K. (2013). Pharmacokinetic and pharmacodynamic interaction of vildagliptin and voglibose in Japanese patients with type 2 diabetes. International Journal of Clinical Pharmacology and Therapeutics, 51(8), 641–651.

Article  CAS  PubMed  Google Scholar 

Ahr, H. J., Boberg, M., Brendel, E., Krause, H. P., & Steinke, W. (1997). Pharmacokinetics of miglitol. Absorption, distribution, metabolism, and excretion following administration to rats, dogs, and man. Arzneimittelforschung, 47(6), 734–745.

CAS  PubMed  Google Scholar 

Morphy, R. (2006). The influence of target family and functional activity on the physicochemical properties of pre-clinical compounds. Journal of Medicinal Chemistry, 49, 2969–2978.

Article  CAS  PubMed  Google Scholar 

Shih, H. P., Zhang, X., & Aronov, A. M. (2018). Drug discovery effectiveness from the standpoint of therapeutic mechanisms and indications. Nat Rev Drug Discovery, 17, 19–33.

Article  CAS  PubMed  Google Scholar 

Karami, T. K., Hailu, S., Feng, S., Graham, R., & Gukasyan, H. J. (2022). Eyes on Lipinski’s rule of five: A new “rule of thumb” for physicochemical design space of ophthalmic drugs. Journal Ocular Pharmacology and Therapeutics, 38(1), 43–55.

Article  CAS  PubMed  Google Scholar 

Morris, G. M., Huey, R., Lindstrom, W., Sanner, M. F., Belew, R. K., Goodsell, D. S., & Olson, A. J. (2009). Autodock4 and AutoDockTools4: Automated docking with selective receptor flexibility. Journal of Computational Chemistry, 16, 2785–2791.

Article  Google Scholar 

Hughes, T. B., & Swamidass, S. J. (2017). Deep learning to predict the formation of quinone species in drug metabolism. Chemical Research in Toxicology, 30(2), 642–656.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Hughes, T. B., Dang, N. L., Miller, G. P., & Swamidass, S. J. (2016). Modeling reactivity to biological macromolecules with a deep multitask network. ACS Central Science, 2(8), 529–537.

Article  CAS  PubMed