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Investigation of salicylidene acylhydrazides derivatives: Molecular Docking, ADMET, and Molecular Dynamic Simulations were used in conjunction towards the design of new Yersinia pseudotuberculosis inhibitors

Year 2022, Volume: 6 Issue: 1, 9 - 30, 15.06.2022

Emmanuel Edache Adamu Uzairu Paul Andrew Mamza Gideon Adamu Shallangwa

https://doi.org/10.33435/tcandtc.1003157
Cited By: 3

Abstract

LysR-type transcription factor RovM is an important target of Yersinia pseudotuberculosis drug discovery and the discovery of antibacterial is considered one of the greatest medical achievements of all time. In this research work, a combination of three docking tools with different algorithms was applied in Salicylidene acylhydrazides derivatives intended toward gram-negative bacterium Yersinia pseudotuberculosis to evaluate their binding interactions.
The analysis of the molecular docking results obtained from the 3-docking software system succeeded in screening twelve fascinating compounds with higher restrictive concentrations having a decent affinity to LysR-type transcription factor RovM macromolecule. Then the Lipinski’s and Veber’s rule properties were calculated to spot the drug-likeness properties of the investigated candidate compounds. To anticipate the toxicity of the predicted candidate chemicals, in-silico toxicity tests were conducted. Furthermore, golden triangle and drug scores were performed, the investigated compounds which fall within the golden triangle indicate that these compounds would not have clearance problems. 5 of the 12 hits drugs pass the golden triangle screening step. These selected drugs undergo a drug score test which only compound 17 passed. To validate the stability, 1 ns molecular dynamic simulations were done on the highest-ranking drug score compound 17 / 3onm complexes. These findings point to interesting avenues for the development of new compounds that are more effective against Yersinia pseudotuberculosis.

Keywords

Yersinia pseudotuberculosis Salicylidene acylhydrazides Docking ADMET golden triangle and MD simulations.

References

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  • [3] P.D. Tamma, S.E. Cosgrove, L.L. Maragakis, Combination Therapy for Treatment of Infections with Gram-Negative Bacteria. Clinical Microbiology Reviews 25(3) (2012) 450–470. doi:10.1128/CMR.05041-11.
  • [4] B. Malgija, H.M. Rajendran, S. Darvin, J. Priyakumari, In silico exploration of HIV entry co-receptor antagonists: a combination of molecular modeling and molecular dynamics simulations. Medicinal Research Reviews 42(3) (2019), 249-253.
  • [5] E.I. Edache, A. Uzairu, P.A. Mamza, G.A. Shallangwa, Molecular Docking Study of Chlamydia Trachomatis Using Salicylidene Acylhydrazides as Inhibitors. Biomedical Journal of Scientific & Technical Research 36(4) (2021) 26472-26489. DOI: 10.26717/BJSTR.2021.36.005895.
  • [6] Z. Hafidi, M.O. El Achouri, F.F. Sousa, L. Pérez, Antifungal activity of amino-alcohols based cationic surfactants and in silico, homology modeling, docking, and molecular dynamics studies against lanosterol 14-α-demethylase enzyme. Journal of Biomolecular Structure and Dynamics (2021) 1-17. https://doi.org/10.1080/07391102.2021.1902396.
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  • [8] J. Davies, D. Davies, Origins and Evolution of Antibiotic Resistance. Microbiology and Molecular Biology Reviews 74(3) (2010) 417- 433. doi:10.1128/MMBR.00016-10.
  • [9] M.P. Koentjoro, N. Ogawa, Structural studies of transcriptional regulation by LysR-TYPE transcriptional Regulators in bacteria. Reviews in Agricultural Science 6 (2018) 105-118. http://dx.doi.org/10.7831/ras.6.105.
  • [10] N. Quade, M. Dieckmann, M. Haffke, A.K. Heroven, P. Dersch, D.W. Heinz, Structure of the effector-binding domain of the LysR-type transcription factor RovM from Yersinia pseudotuberculosis. Acta Crystallographica Section D 67 (2011) 81–90. doi:10.1107/S0907444910049681.
  • [11] E.I. Edache, A. Uzairu, P.A. Mamza, G.A. Shallangwa, Docking Simulations, and Virtual Screening to find Novel Ligands for T3S in Yersinia pseudotuberculosis YPIII, A drug target for type III secretion (T3S) in the Gram-negative pathogen Yersinia pseudotuberculosis. Chemical Review and Letters 4 (2021) 130-144. doi: 10.22034/CRL.2021.254804.1088.
  • [12] W. Sobhi, A. Attoui, T. Lemaoui, A. Erto, Y. Benguerba, In silico drug discovery of Acetylcholinesterase and Butyrylcholinesterase enzymes inhibitors based on Quantitative Structure-Activity Relationship (QSAR) and drug-likeness evaluation. Journal of Molecular Structure 1229 (2021) 129845.
  • [13] R. Shivakumar, K. Venkatarangaiah, S. Shastri, R.B. Nagaraja, A. Sheshagiri, Antibacterial Property and Molecular Docking Studies of Leaf Calli Phytochemicals of Bridelia scandens Wild. Pharmacognosy Journal 10(6) (2018) 1221-1229. DOI: 10.5530/pj.2018.6.209
  • [14] M.H. Baig, K. Ahmad, G. Rabbani, M. Danishuddin, I. Choi, Computer-Aided Drug Design and its Application to the Development of Potential Drugs for Neurodegenerative Disorders. Current Neuropharmacology 16 (2018) 740-748. DOI: 10.2174/1570159X15666171016163510.
  • [15] M. Mahdavia, V. Moreau, In silico designing breast cancer peptide vaccine for binding to MHC class I and II: A molecular docking study. Computational Biology and Chemistry - Journals 65 (2016) 110–116. http://dx.doi.org/10.1016/j.compbiolchem.2016.10.007.
  • [16] R. Ganesh, I. Kannan, Molecular Docking Study of Certain Plant Alkaloid Derivatives as Inhibitors of Various Drug Targets of Alzheimer’s Disease. Biomedical and Pharmacology Journal 10(3) (2017) 1489-1494. http://dx.doi.org/10.13005/bpj/1257.
  • [17] T.W. Johnson, K.R. Dress, M. Edwards, Using the Golden Triangle to optimize clearance and oral absorption. Bioorganic & Medicinal Chemistry Letters 19 (2009) 5560–5564, doi: 10.1016/j.bmcl.2009.08.045.
  • [18] E.I. Edache, A. Uzairu, P.A. Mamza, G.A. Shallangwa, Prediction of HemO Inhibitors Based on Iminoguanidine using QSAR, 3DQSAR Study, Molecular Docking, Molecular Dynamic Simulation, and ADMET. Journal of Drug Design and Discovery Research 1(2) (2020) 36-52.
  • [19] P. Munikumar, P. Natarajan, U. Amineni, R.K.V. Krishna, Discovery of potential lumazine synthase antagonists for pathogens involved in bacterial meningitis: In silico study. Informatics in Medicine Unlocked 15 (2019) 100187. https://doi.org/10.1016/j.imu.2019.100187.
  • [20] N. Quade, M. Dieckmann, M. Haffke, A.K. Heroven, P. Dersch, D.W. Heinz, Structure of the effector-binding domain of the LysR-type transcription factor RovM from Yersinia pseudotuberculosis. Acta Crystallographica Section D 67(Pt2) (2011) 81-90. doi: 10.1107/S0907444910049681.
  • [21] R. Thomsen, M.H. Christensen, MolDock: a new technique for high-accuracy molecular docking. Journal of Medicinal Chemistry 49(11) (2006) 3315-21. doi: 10.1021/jm051197e.
  • [22] K.C. Hsu, Y.F Chen, S.R. Lin, J.M. Yang, iGemDock: a graphical environment of enhancing GEMDOCK using pharmacological interactions and post-screening analysis, BMC Bioinformatics 2(1) (2011) 1-11.
  • [23] O. Trott, A.J. Olson, AutoDock Vina: Improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading. Journal of Computational Chemistry 31 (2009) 455–461. https://doi.org/10.1002/jcc.21334.
  • [24] M.J. Waring, Defining optimum lipophilicity and molecular weight ranges for drug candidates-Molecular weight dependent lower log D limits based on permeability, Bioorganic & Medicinal Chemistry Letters 19 (2009) 2844–2851. doi: 10.1016/j.bmcl.2009.03.109.
  • [25] A. Zerroug, S. Belaidi, I. Benbrahim, L. Sinha, S. Chtita, Virtual screening in drug-likeness and structure/activity relationship of pyridazine derivatives as Anti-Alzheimer drugs, Journal of King Saud University-Science 31 (2019) 595–601. doi: 10.1016/j.jksus.2018.03.024.
  • [26] J.C. Phillips, R. Braun, W. Wang, J. Gumbart, E. Tajkhorshid, E. Villa, C. Chipot, R.D. Skeel, L. Kale, K. Schulten, Scalable molecular dynamics with NAMD. Journal of Computational Chemistry 26 (2005) 1781–1802.
  • [27] W. Humphrey, A. Dalke, K. Schulten, “VMD – Visual Molecular Dynamics”, Journal of Molecular Graphics 14 (1996) 33-38.
  • [28] 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 46(1-3) (2001) 3-26.
  • [29] D.F. Veber, S.R. Johnson, H.Y. Cheng, B.R. Smith, K.W. Ward, K.D. Kopple, Molecular properties that influence the oral bioavailability of drug candidates, Journal of Medicinal Chemistry 45(12) (2002) 2615-2623.
  • [30] T.S. Maliehe, P.H. Tsilo, J.S. Shandu, Computational Evaluation of ADMET Properties and Bioactive Score of Compounds from Encephalartos ferox. Pharmacognosy Journal 12(6) (2020) 1357-62. DOI: 10.5530/pj.2020.12.187.
  • [31] H. Pajouhesh, G.R. Lenz, Medicinal chemical properties of successful central nervous system drugs. Neuro Rx. 2 (2005) 541–553. DOI: 10.1602/neurorx.2.4.541.
  • [32] T.V.A. Kumar, S. Kabilan, V. Parthasarathy, Screening and Toxicity Risk Assessment of Selected Compounds to Target Cancer using QSAR and Pharmacophore Modelling. International Journal of PharmTech Research 10(4) (2017) 219-224. http://dx.doi.org/10.20902/IJPTR.2017.10428.
  • [33] O. Ursu, A. Rayan, A. Goldblum, T.I. Oprea, Understanding drug-likeness. WIREs Computational Molecular Science 1 (2011) 760-781. doi:10.1002/wcms.52.
  • [34] M. von Korff, T. Sander, Toxicity-indicating structural patterns. Journal of Chemical Information and Modeling 46 (2) (2006) 536-544.
  • [35] S.A. Egieyeh, J. Syce, S.F. Malan, A. Christoffels, Prioritization of anti‑malarial hits from nature: chemo‑informatic profiling of natural products with in vitro antiplasmodial activities and currently registered anti‑malarial drugs. Malaria Journal 15(50) (2016) 1-23. DOI 10.1186/s12936-016-1087-y.
  • [36] M. Ouassaf, S. Belaidi, I. benbrahim, H. Belaidi, S. Chtita, Quantitative Structure-Activity Relationships of 1.2.3 Triazole Derivatives as Aromatase Inhibition Activity. Turkish Computational and Theoretical Chemistry 4(1) (2020) 1-11. doi.org/10.33435/tcandtc.545369.
  • [37] G.M. Keseru, G.M. Makara, The influence of lead discovery strategies on the properties of drug candidates. Nature Reviews Drug Discovery 3 (2009) 203-212. doi: 10.1038/nrd2796.
  • [38] C. Forrey, J.F. Douglasb, M.K. Gilson, The fundamental role of flexibility on the strength of molecular binding. Soft Matter 8 (2012) 6385-6392. DOI: 10.1039/C2SM25160D.
  • [39] F.Z. Fadel, N. Tchouar, S. Belaidi, V. Soualmia, O. Oukil, K. Ouadah, Computational Screening and QSAR Study on a Series of Theophylline Derivatives as Aldh1a1 Inhibitors. Journal of Fundamental and Applied Sciences 13(2) (2021) 942-964. doi: http://dx.doi.org/10.43 14/jfas.v13i2.17.
  • [40] E.H. Kerns, D. Li, Drug-Like Properties: Concepts, Structure Design and Methods from ADME to Toxicity Optimization. Elsevier, United States (2008) 86-98.
  • [41] D.E. Clarke, J.S. Delaney, Physical and molecular properties of agrochemicals: An analysis of screen inputs, hits, leads, and products, CHIMIA 57(11) (2003) 731-734.
  • [42] E.I. Edache, A. Uzairu, P.A. Mamza, G.A. Shallangwa, Molecular docking, molecular dynamics simulations, and ADME study to identify inhibitors of Crimean-Congo Hemorrhagic Fever (CCHF) viral ovarian tumor domain protease (vOTU). Chemistry Research Journal 5(5) (2020) 16-30.
  • [43] H. Alonso, A.A. Bliznyuk, J.E. Gready, Combining Docking and Molecular Dynamic Simulations in Drug Design. Medicinal Research Reviews 26 (2006) 531-568. doi:10.1002/med.20067.

Year 2022, Volume: 6 Issue: 1, 9 - 30, 15.06.2022

Emmanuel Edache Adamu Uzairu Paul Andrew Mamza Gideon Adamu Shallangwa

https://doi.org/10.33435/tcandtc.1003157
Cited By: 3

Abstract

References

  • [1] A.K. Heroven, P. Dersch, RovM, a novel LysR-type regulator of the virulence activator gene rovA, controls cell invasion, virulence, and motility of Yersinia pseudotuberculosis. Molecular Microbiology 62(5) (2006)1469-1483. doi: 10.1111/j.1365-2958.2006.05458.x.
  • [2] A.K. Heroven, A.M. Nuss, P. Dersch "RNA-based mechanisms of virulence control in Enterobacteriaceae." RNA Biology 14.5 (2017) 471-487.
  • [3] P.D. Tamma, S.E. Cosgrove, L.L. Maragakis, Combination Therapy for Treatment of Infections with Gram-Negative Bacteria. Clinical Microbiology Reviews 25(3) (2012) 450–470. doi:10.1128/CMR.05041-11.
  • [4] B. Malgija, H.M. Rajendran, S. Darvin, J. Priyakumari, In silico exploration of HIV entry co-receptor antagonists: a combination of molecular modeling and molecular dynamics simulations. Medicinal Research Reviews 42(3) (2019), 249-253.
  • [5] E.I. Edache, A. Uzairu, P.A. Mamza, G.A. Shallangwa, Molecular Docking Study of Chlamydia Trachomatis Using Salicylidene Acylhydrazides as Inhibitors. Biomedical Journal of Scientific & Technical Research 36(4) (2021) 26472-26489. DOI: 10.26717/BJSTR.2021.36.005895.
  • [6] Z. Hafidi, M.O. El Achouri, F.F. Sousa, L. Pérez, Antifungal activity of amino-alcohols based cationic surfactants and in silico, homology modeling, docking, and molecular dynamics studies against lanosterol 14-α-demethylase enzyme. Journal of Biomolecular Structure and Dynamics (2021) 1-17. https://doi.org/10.1080/07391102.2021.1902396.
  • [7] Q. Zhang, G. Lambert, D. Liao, H. Kim, K. Robin, C.K. Tung, N. Pourmand, R.H. Austin, Acceleration of emergence of bacterial antibiotic resistance in connected microenvironments. Science (New York, NY), 333 (2011) 1764–1767. https://doi.org/10.1126/science.1208747
  • [8] J. Davies, D. Davies, Origins and Evolution of Antibiotic Resistance. Microbiology and Molecular Biology Reviews 74(3) (2010) 417- 433. doi:10.1128/MMBR.00016-10.
  • [9] M.P. Koentjoro, N. Ogawa, Structural studies of transcriptional regulation by LysR-TYPE transcriptional Regulators in bacteria. Reviews in Agricultural Science 6 (2018) 105-118. http://dx.doi.org/10.7831/ras.6.105.
  • [10] N. Quade, M. Dieckmann, M. Haffke, A.K. Heroven, P. Dersch, D.W. Heinz, Structure of the effector-binding domain of the LysR-type transcription factor RovM from Yersinia pseudotuberculosis. Acta Crystallographica Section D 67 (2011) 81–90. doi:10.1107/S0907444910049681.
  • [11] E.I. Edache, A. Uzairu, P.A. Mamza, G.A. Shallangwa, Docking Simulations, and Virtual Screening to find Novel Ligands for T3S in Yersinia pseudotuberculosis YPIII, A drug target for type III secretion (T3S) in the Gram-negative pathogen Yersinia pseudotuberculosis. Chemical Review and Letters 4 (2021) 130-144. doi: 10.22034/CRL.2021.254804.1088.
  • [12] W. Sobhi, A. Attoui, T. Lemaoui, A. Erto, Y. Benguerba, In silico drug discovery of Acetylcholinesterase and Butyrylcholinesterase enzymes inhibitors based on Quantitative Structure-Activity Relationship (QSAR) and drug-likeness evaluation. Journal of Molecular Structure 1229 (2021) 129845.
  • [13] R. Shivakumar, K. Venkatarangaiah, S. Shastri, R.B. Nagaraja, A. Sheshagiri, Antibacterial Property and Molecular Docking Studies of Leaf Calli Phytochemicals of Bridelia scandens Wild. Pharmacognosy Journal 10(6) (2018) 1221-1229. DOI: 10.5530/pj.2018.6.209
  • [14] M.H. Baig, K. Ahmad, G. Rabbani, M. Danishuddin, I. Choi, Computer-Aided Drug Design and its Application to the Development of Potential Drugs for Neurodegenerative Disorders. Current Neuropharmacology 16 (2018) 740-748. DOI: 10.2174/1570159X15666171016163510.
  • [15] M. Mahdavia, V. Moreau, In silico designing breast cancer peptide vaccine for binding to MHC class I and II: A molecular docking study. Computational Biology and Chemistry - Journals 65 (2016) 110–116. http://dx.doi.org/10.1016/j.compbiolchem.2016.10.007.
  • [16] R. Ganesh, I. Kannan, Molecular Docking Study of Certain Plant Alkaloid Derivatives as Inhibitors of Various Drug Targets of Alzheimer’s Disease. Biomedical and Pharmacology Journal 10(3) (2017) 1489-1494. http://dx.doi.org/10.13005/bpj/1257.
  • [17] T.W. Johnson, K.R. Dress, M. Edwards, Using the Golden Triangle to optimize clearance and oral absorption. Bioorganic & Medicinal Chemistry Letters 19 (2009) 5560–5564, doi: 10.1016/j.bmcl.2009.08.045.
  • [18] E.I. Edache, A. Uzairu, P.A. Mamza, G.A. Shallangwa, Prediction of HemO Inhibitors Based on Iminoguanidine using QSAR, 3DQSAR Study, Molecular Docking, Molecular Dynamic Simulation, and ADMET. Journal of Drug Design and Discovery Research 1(2) (2020) 36-52.
  • [19] P. Munikumar, P. Natarajan, U. Amineni, R.K.V. Krishna, Discovery of potential lumazine synthase antagonists for pathogens involved in bacterial meningitis: In silico study. Informatics in Medicine Unlocked 15 (2019) 100187. https://doi.org/10.1016/j.imu.2019.100187.
  • [20] N. Quade, M. Dieckmann, M. Haffke, A.K. Heroven, P. Dersch, D.W. Heinz, Structure of the effector-binding domain of the LysR-type transcription factor RovM from Yersinia pseudotuberculosis. Acta Crystallographica Section D 67(Pt2) (2011) 81-90. doi: 10.1107/S0907444910049681.
  • [21] R. Thomsen, M.H. Christensen, MolDock: a new technique for high-accuracy molecular docking. Journal of Medicinal Chemistry 49(11) (2006) 3315-21. doi: 10.1021/jm051197e.
  • [22] K.C. Hsu, Y.F Chen, S.R. Lin, J.M. Yang, iGemDock: a graphical environment of enhancing GEMDOCK using pharmacological interactions and post-screening analysis, BMC Bioinformatics 2(1) (2011) 1-11.
  • [23] O. Trott, A.J. Olson, AutoDock Vina: Improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading. Journal of Computational Chemistry 31 (2009) 455–461. https://doi.org/10.1002/jcc.21334.
  • [24] M.J. Waring, Defining optimum lipophilicity and molecular weight ranges for drug candidates-Molecular weight dependent lower log D limits based on permeability, Bioorganic & Medicinal Chemistry Letters 19 (2009) 2844–2851. doi: 10.1016/j.bmcl.2009.03.109.
  • [25] A. Zerroug, S. Belaidi, I. Benbrahim, L. Sinha, S. Chtita, Virtual screening in drug-likeness and structure/activity relationship of pyridazine derivatives as Anti-Alzheimer drugs, Journal of King Saud University-Science 31 (2019) 595–601. doi: 10.1016/j.jksus.2018.03.024.
  • [26] J.C. Phillips, R. Braun, W. Wang, J. Gumbart, E. Tajkhorshid, E. Villa, C. Chipot, R.D. Skeel, L. Kale, K. Schulten, Scalable molecular dynamics with NAMD. Journal of Computational Chemistry 26 (2005) 1781–1802.
  • [27] W. Humphrey, A. Dalke, K. Schulten, “VMD – Visual Molecular Dynamics”, Journal of Molecular Graphics 14 (1996) 33-38.
  • [28] 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 46(1-3) (2001) 3-26.
  • [29] D.F. Veber, S.R. Johnson, H.Y. Cheng, B.R. Smith, K.W. Ward, K.D. Kopple, Molecular properties that influence the oral bioavailability of drug candidates, Journal of Medicinal Chemistry 45(12) (2002) 2615-2623.
  • [30] T.S. Maliehe, P.H. Tsilo, J.S. Shandu, Computational Evaluation of ADMET Properties and Bioactive Score of Compounds from Encephalartos ferox. Pharmacognosy Journal 12(6) (2020) 1357-62. DOI: 10.5530/pj.2020.12.187.
  • [31] H. Pajouhesh, G.R. Lenz, Medicinal chemical properties of successful central nervous system drugs. Neuro Rx. 2 (2005) 541–553. DOI: 10.1602/neurorx.2.4.541.
  • [32] T.V.A. Kumar, S. Kabilan, V. Parthasarathy, Screening and Toxicity Risk Assessment of Selected Compounds to Target Cancer using QSAR and Pharmacophore Modelling. International Journal of PharmTech Research 10(4) (2017) 219-224. http://dx.doi.org/10.20902/IJPTR.2017.10428.
  • [33] O. Ursu, A. Rayan, A. Goldblum, T.I. Oprea, Understanding drug-likeness. WIREs Computational Molecular Science 1 (2011) 760-781. doi:10.1002/wcms.52.
  • [34] M. von Korff, T. Sander, Toxicity-indicating structural patterns. Journal of Chemical Information and Modeling 46 (2) (2006) 536-544.
  • [35] S.A. Egieyeh, J. Syce, S.F. Malan, A. Christoffels, Prioritization of anti‑malarial hits from nature: chemo‑informatic profiling of natural products with in vitro antiplasmodial activities and currently registered anti‑malarial drugs. Malaria Journal 15(50) (2016) 1-23. DOI 10.1186/s12936-016-1087-y.
  • [36] M. Ouassaf, S. Belaidi, I. benbrahim, H. Belaidi, S. Chtita, Quantitative Structure-Activity Relationships of 1.2.3 Triazole Derivatives as Aromatase Inhibition Activity. Turkish Computational and Theoretical Chemistry 4(1) (2020) 1-11. doi.org/10.33435/tcandtc.545369.
  • [37] G.M. Keseru, G.M. Makara, The influence of lead discovery strategies on the properties of drug candidates. Nature Reviews Drug Discovery 3 (2009) 203-212. doi: 10.1038/nrd2796.
  • [38] C. Forrey, J.F. Douglasb, M.K. Gilson, The fundamental role of flexibility on the strength of molecular binding. Soft Matter 8 (2012) 6385-6392. DOI: 10.1039/C2SM25160D.
  • [39] F.Z. Fadel, N. Tchouar, S. Belaidi, V. Soualmia, O. Oukil, K. Ouadah, Computational Screening and QSAR Study on a Series of Theophylline Derivatives as Aldh1a1 Inhibitors. Journal of Fundamental and Applied Sciences 13(2) (2021) 942-964. doi: http://dx.doi.org/10.43 14/jfas.v13i2.17.
  • [40] E.H. Kerns, D. Li, Drug-Like Properties: Concepts, Structure Design and Methods from ADME to Toxicity Optimization. Elsevier, United States (2008) 86-98.
  • [41] D.E. Clarke, J.S. Delaney, Physical and molecular properties of agrochemicals: An analysis of screen inputs, hits, leads, and products, CHIMIA 57(11) (2003) 731-734.
  • [42] E.I. Edache, A. Uzairu, P.A. Mamza, G.A. Shallangwa, Molecular docking, molecular dynamics simulations, and ADME study to identify inhibitors of Crimean-Congo Hemorrhagic Fever (CCHF) viral ovarian tumor domain protease (vOTU). Chemistry Research Journal 5(5) (2020) 16-30.
  • [43] H. Alonso, A.A. Bliznyuk, J.E. Gready, Combining Docking and Molecular Dynamic Simulations in Drug Design. Medicinal Research Reviews 26 (2006) 531-568. doi:10.1002/med.20067.
There are 43 citations in total.

Details

Primary Language English
Subjects Chemical Engineering
Journal Section Research Article
Authors

Emmanuel Edache 0000-0002-5485-0583

Adamu Uzairu This is me

Paul Andrew Mamza This is me

Gideon Adamu Shallangwa

Early Pub Date March 15, 2022
Publication Date June 15, 2022
Submission Date October 2, 2021
Published in Issue Year 2022 Volume: 6 Issue: 1

Cite

APA Edache, E., Uzairu, A., Mamza, P. A., Shallangwa, G. A. (2022). Investigation of salicylidene acylhydrazides derivatives: Molecular Docking, ADMET, and Molecular Dynamic Simulations were used in conjunction towards the design of new Yersinia pseudotuberculosis inhibitors. Turkish Computational and Theoretical Chemistry, 6(1), 9-30. https://doi.org/10.33435/tcandtc.1003157
AMA Edache E, Uzairu A, Mamza PA, Shallangwa GA. Investigation of salicylidene acylhydrazides derivatives: Molecular Docking, ADMET, and Molecular Dynamic Simulations were used in conjunction towards the design of new Yersinia pseudotuberculosis inhibitors. Turkish Comp Theo Chem (TC&TC). June 2022;6(1):9-30. doi:10.33435/tcandtc.1003157
Chicago Edache, Emmanuel, Adamu Uzairu, Paul Andrew Mamza, and Gideon Adamu Shallangwa. “Investigation of Salicylidene Acylhydrazides Derivatives: Molecular Docking, ADMET, and Molecular Dynamic Simulations Were Used in Conjunction towards the Design of New Yersinia Pseudotuberculosis Inhibitors”. Turkish Computational and Theoretical Chemistry 6, no. 1 (June 2022): 9-30. https://doi.org/10.33435/tcandtc.1003157.
EndNote Edache E, Uzairu A, Mamza PA, Shallangwa GA (June 1, 2022) Investigation of salicylidene acylhydrazides derivatives: Molecular Docking, ADMET, and Molecular Dynamic Simulations were used in conjunction towards the design of new Yersinia pseudotuberculosis inhibitors. Turkish Computational and Theoretical Chemistry 6 1 9–30.
IEEE E. Edache, A. Uzairu, P. A. Mamza, and G. A. Shallangwa, “Investigation of salicylidene acylhydrazides derivatives: Molecular Docking, ADMET, and Molecular Dynamic Simulations were used in conjunction towards the design of new Yersinia pseudotuberculosis inhibitors”, Turkish Comp Theo Chem (TC&TC), vol. 6, no. 1, pp. 9–30, 2022, doi: 10.33435/tcandtc.1003157.
ISNAD Edache, Emmanuel et al. “Investigation of Salicylidene Acylhydrazides Derivatives: Molecular Docking, ADMET, and Molecular Dynamic Simulations Were Used in Conjunction towards the Design of New Yersinia Pseudotuberculosis Inhibitors”. Turkish Computational and Theoretical Chemistry 6/1 (June 2022), 9-30. https://doi.org/10.33435/tcandtc.1003157.
JAMA Edache E, Uzairu A, Mamza PA, Shallangwa GA. Investigation of salicylidene acylhydrazides derivatives: Molecular Docking, ADMET, and Molecular Dynamic Simulations were used in conjunction towards the design of new Yersinia pseudotuberculosis inhibitors. Turkish Comp Theo Chem (TC&TC). 2022;6:9–30.
MLA Edache, Emmanuel et al. “Investigation of Salicylidene Acylhydrazides Derivatives: Molecular Docking, ADMET, and Molecular Dynamic Simulations Were Used in Conjunction towards the Design of New Yersinia Pseudotuberculosis Inhibitors”. Turkish Computational and Theoretical Chemistry, vol. 6, no. 1, 2022, pp. 9-30, doi:10.33435/tcandtc.1003157.
Vancouver Edache E, Uzairu A, Mamza PA, Shallangwa GA. Investigation of salicylidene acylhydrazides derivatives: Molecular Docking, ADMET, and Molecular Dynamic Simulations were used in conjunction towards the design of new Yersinia pseudotuberculosis inhibitors. Turkish Comp Theo Chem (TC&TC). 2022;6(1):9-30.

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Journal Full Title: Turkish Computational and Theoretical Chemistry


Journal Abbreviated Title: Turkish Comp Theo Chem (TC&TC)