請用此 Handle URI 來引用此文件：
http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/90153完整後設資料紀錄
| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 席行正 | zh_TW |
| dc.contributor.advisor | Hsing-Cheng Hsi | en |
| dc.contributor.author | 紀淑君 | zh_TW |
| dc.contributor.author | Shu-Chun Chi | en |
| dc.date.accessioned | 2023-09-22T17:38:21Z | - |
| dc.date.available | 2023-11-09 | - |
| dc.date.copyright | 2023-09-22 | - |
| dc.date.issued | 2023 | - |
| dc.date.submitted | 2023-08-10 | - |
| dc.identifier.citation | Abdulfatai, U., Uzairu, A., & Uba, S. (2018). Molecular docking and QSAR analysis of a few Gama amino butyric acid aminotransferase inhibitors. Egyptian Journal of Basic and Applied Sciences, 5(1), 41-53. doi:10.1016/j.ejbas.2018.01.003
Akinola, L. K., Uzairu, A., Shallangwa, G. A., & Abechi, S. E. (2023). Quantitative structure–activity relationship modeling of hydroxylated polychlorinated biphenyls as constitutive androstane receptor agonists. Structural Chemistry, 34(2), 477-490. doi:10.1007/s11224-022-01992-2 Alisi, I., Uzairu, A., & Stephen, A. (2019). Design and Validation of Predictive Antioxidant Models for Chalcones. FUDMA Records of Chemical Sciences 1(2): 48–60. Altaf, R., Nadeem, H., Iqbal, M. N., Ilyas, U., Ashraf, Z., Imran, M., & Muhammad, S. A. (2022). Synthesis, Biological Evaluation, 2D-QSAR, and Molecular Simulation Studies of Dihydropyrimidinone Derivatives as Alkaline Phosphatase Inhibitors. ACS Omega, 7(8), 7139-7154. doi:10.1021/acsomega.1c06833 Ambure, P., Gajewicz-Skretna, A., Cordeiro, M. N. D. S., & Roy, K. (2019). New Workflow for QSAR Model Development from Small Data Sets: Small Dataset Curator and Small Dataset Modeler. Integration of Data Curation, Exhaustive Double Cross-Validation, and a Set of Optimal Model Selection Techniques. Journal of Chemical Information and Modeling, 59(10), 4070-4076. doi:10.1021/acs.jcim.9b00476 Ammouchi, N., Allal, H., Belhocine, Y., Bettaz, S., & Zouaoui, E. (2020). DFT computations and molecular dynamics investigations on conformers of some pyrazinamide derivatives as corrosion inhibitors for aluminum. Journal of Molecular Liquids, 300, 112309. doi:10.1016/j.molliq.2019.112309 Ankley, G. T., Bennett, R. S., Erickson, R. J., Hoff, D. J., Hornung, M. W., Johnson, R. D., Villeneuve, D. L. (2010). Adverse outcome pathways: a conceptual framework to support ecotoxicology research and risk assessment. Environ Toxicol Chem, 29(3), 730-741. doi:10.1002/etc.34 Bary, G., Ghani, L., Jamil, M. I., Arslan, M., Ahmed, W., Ahmad, A., Huang, D. (2021). Designing small organic non-fullerene acceptor molecules with diflorobenzene or quinoline core and dithiophene donor moiety through density functional theory. Scientific Reports, 11(1), 19683. doi:10.1038/s41598-021-97662-0 Cao, H., Wang, F., Liang, Y., Wang, H., Zhang, A., & Song, M. (2017). Experimental and computational insights on the recognition mechanism between the estrogen receptor α with bisphenol compounds. Arch Toxicol, 91(12), 3897-3912. doi:10.1007/s00204-017-2011-0 Cao, M., Wei, J., Pan, Y., Wang, L., Li, Z., Hu, Y., Cao, H. (2023). Antagonistic mechanisms of bisphenol analogues on the estrogen receptor α in zebrafish embryos: Experimental and computational studies. Sci Total Environ, 857(1), 159259. doi:10.1016/j.scitotenv.2022.159259 Casida, M. E. (1999). Correlated optimized effective-potential treatment of the derivative discontinuity and of the highest occupied Kohn-Sham eigenvalue: A Janak-type theorem for the optimized effective-potential model. Physical Review B, 59(7), 4694-4698. doi:10.1103/PhysRevB.59.4694 Celik, L., Lund, J. D. D., & Schiøtt, B. (2008). Exploring Interactions of Endocrine-Disrupting Compounds with Different Conformations of the Human Estrogen Receptor α Ligand Binding Domain: A Molecular Docking Study. Chemical Research in Toxicology, 21(11), 2195-2206. doi:10.1021/tx800278d Çelik, M. S., Çetinus, Ş. A., Yenidünya, A. F., Çetinkaya, S., & Tüzün, B. (2023). Biosorption of Rhodamine B dye from aqueous solution by Rhus coriaria L. plant: Equilibrium, kinetic, thermodynamic and DFT calculations. Journal of Molecular Structure, 1272, 134158. doi:10.1016/j.molstruc.2022.134158 Chakraborty, D., & Chattaraj, P. K. (2021). Conceptual density functional theory based electronic structure principles. Chemical Science, 12(18), 6264-6279. doi:10.1039/D0SC07017C Chen, Y., Yang, J., Yao, B., Zhi, D., Luo, L., & Zhou, Y. (2022). Endocrine disrupting chemicals in the environment: Environmental sources, biological effects, remediation techniques, and perspective. Environ Pollut, 310, 119918. doi:10.1016/j.envpol.2022.119918 Chen, Y., Yang, J., Yao, B., Zhi, D., Luo, L., & Zhou, Y. (2022). Endocrine disrupting chemicals in the environment: Environmental sources, biological effects, remediation techniques, and perspective. Environmental Pollution, 310, 119918. doi:10.1016/j.envpol.2022.119918 Chiodi, D., & Ishihara, Y. (2023). “Magic Chloro”: Profound Effects of the Chlorine Atom in Drug Discovery. Journal of Medicinal Chemistry, 66(8), 5305-5331. doi:10.1021/acs.jmedchem.2c02015 Czernych, R., Chraniuk, M., Zagożdżon, P., & Wolska, L. (2017). Characterization of estrogenic and androgenic activity of phthalates by the XenoScreen YES/YAS in vitro assay. Environ Toxicol Pharmacol, 53, 95-104. doi:10.1016/j.etap.2017.05.010 Deghady, A. M., Hussein, R. K., Alhamzani, A. G., & Mera, A. (2021). Density Functional Theory and Molecular Docking Investigations of the Chemical and Antibacterial Activities for 1-(4-Hydroxyphenyl)-3-phenylprop-2-en-1-one. Molecules, 26(12), 3631. doi:10.3390/molecules26123631 Delfosse, V., Grimaldi, M., Cavaillès, V., Balaguer, P., & Bourguet, W. (2014). Structural and functional profiling of environmental ligands for estrogen receptors. Environ Health Perspect, 122(12), 1306-1313. doi:10.1289/ehp.1408453 Diamanti-Kandarakis, E., Bourguignon, J. P., Giudice, L. C., Hauser, R., Prins, G. S., Soto, A. M., Gore, A. C. (2009). Endocrine-disrupting chemicals: an Endocrine Society scientific statement. Endocr Rev, 30(4), 293-342. doi:10.1210/er.2009-0002 Ding, D., Xu, L., Fang, H., Hong, H., Perkins, R., Harris, S., Tong, W. (2010). The EDKB: an established knowledge base for endocrine disrupting chemicals. BMC Bioinformatics, 11 Suppl 6(Suppl 6), S5. doi:10.1186/1471-2105-11-s6-s5 Domingo, L. R., Ríos-Gutiérrez, M., & Pérez, P. (2016). Applications of the Conceptual Density Functional Theory Indices to Organic Chemistry Reactivity. Molecules, 21(6). doi:10.3390/molecules21060748 Edache, E. I., Uzairu, A., Mamza, P. A., & Shallangwa, G. A. (2022). Structure-based simulated scanning of rheumatoid arthritis inhibitors: 2D-QSAR, 3D-QSAR, docking, molecular dynamics simulation, and lipophilicity indices calculation. Scientific African, 15, e01088. doi:10.1016/j.sciaf.2021.e01088 Faver, J., & Merz, K. M., Jr. (2010). Utility of the Hard/Soft Acid−Base Principle via the Fukui Function in Biological Systems. Journal of Chemical Theory and Computation, 6(2), 548-559. doi:10.1021/ct9005085 Frimayanti, N., Octaviani, M., Wulandari, G., & Zamri, A. (2023). Estimation of Hydrazone Derivative Compounds as Anti-Bacterial Agents Against Staphylococcus aureous Through Molecular Docking and Self Consisted Field. Trends in Sciences, 20(8). doi:10.48048/tis.2023.5593 Frisch, M. J., Trucks, G. W., Schlegel, H. B., Scuseria, G. E., Robb, M. A., Cheeseman, J. R., Fox, D. J. (2016). Gaussian 16 Rev. C.01. Wallingford, CT. Gackowski, M., Szewczyk-Golec, K., Pluskota, R., Koba, M., Mądra-Gackowska, K., & Woźniak, A. (2022). Application of Multivariate Adaptive Regression Splines (MARSplines) for Predicting Antitumor Activity of Anthrapyrazole Derivatives. International Journal of Molecular Sciences, 23(9), 5132. doi:10.3390/ijms23095132 Gao, L., Tu, Y., Ågren, H., & Eriksson, L. A. (2012). Characterization of Agonist Binding to His524 in the Estrogen Receptor α Ligand Binding Domain. The Journal of Physical Chemistry B, 116(16), 4823-4830. doi:10.1021/jp300895g Geerlings, P. (2022). From Density Functional Theory to Conceptual Density Functional Theory and Biosystems. Pharmaceuticals (Basel), 15(9). doi:10.3390/ph15091112 Geerlings, P., Chamorro, E., Chattaraj, P. K., De Proft, F., Gázquez, J. L., Liu, S., Ayers, P. (2020). Conceptual density functional theory: status, prospects, issues. Theoretical Chemistry Accounts, 139(2), 36. doi:10.1007/s00214-020-2546-7 Gobec, S., Sova, M., Kristan, K., & Rizner, T. L. (2004). Cinnamic acid esters as potent inhibitors of fungal 17beta-hydroxysteroid dehydrogenase--a model enzyme of the short-chain dehydrogenase/reductase superfamily. Bioorg Med Chem Lett, 14(15), 3933-3936. doi:10.1016/j.bmcl.2004.05.069 Godfray, H. C. J., Stephens, A. E. A., Jepson, P. D., Jobling, S., Johnson, A. C., Matthiessen, P., McLean, A. R. (2019). A restatement of the natural science evidence base on the effects of endocrine disrupting chemicals on wildlife. Proc Biol Sci, 286(1897), 20182416. doi:10.1098/rspb.2018.2416 Goya-Jorge, E., Amber, M., Gozalbes, R., Connolly, L., & Barigye, S. J. (2021). Assessing the chemical-induced estrogenicity using in silico and in vitro methods. Environ Toxicol Pharmacol, 87, 103688. doi:10.1016/j.etap.2021.103688 Grasso, G., Di Gregorio, A., Mavkov, B., Piga, D., Labate, G. F. D. U., Danani, A., & Deriu, M. A. (2022). Fragmented blind docking: a novel protein–ligand binding prediction protocol. Journal of Biomolecular Structure and Dynamics, 40(24), 13472-13481. doi:10.1080/07391102.2021.1988709 Gubskaya, A. V., & Kusalik, P. G. (2002). The total molecular dipole moment for liquid water. The Journal of Chemical Physics, 117(11), 5290-5302. doi:10.1063/1.1501122 He, W., Zhang, W., Chu, Z., & Li, Y. (2021). Mitigating the Adverse Effects of Polychlorinated Biphenyl Derivatives on Estrogenic Activity via Molecular Modification Techniques. Int J Environ Res Public Health, 18(9). doi:10.3390/ijerph18094999 Heo, S., Safder, U., & Yoo, C. (2019). Deep learning driven QSAR model for environmental toxicology: Effects of endocrine disrupting chemicals on human health. Environmental Pollution, 253, 29-38. doi:10.1016/j.envpol.2019.06.081 Himo, F., & de Visser, S. P. (2022). Status report on the quantum chemical cluster approach for modeling enzyme reactions. Communications Chemistry, 5(1), 29. doi:10.1038/s42004-022-00642-2 Hollas, B. (2003). An Analysis of the Autocorrelation Descriptor for Molecules. Journal of Mathematical Chemistry, 33, 91-101. doi:10.1023/A:1023247831238 Ilia, M., Ivan, D., Nikolai, D., & Ivanka, K. (2016). Synthesis, Toxicological, Anticoagulant Activity and Quantumchemical Study of Bis-coumarins and Other Coumarin Derivatives. World Journal of Research and Review, 3(4). Jasrotia, R., Langer, S., & Dhar, M. (2021). Endocrine Disrupting Chemicals in Aquatic Ecosystem: An Emerging Threat to Wildlife and Human Health. Proceedings of the Zoological Society, 74(4), 634-647. doi:10.1007/s12595-021-00410-5 Jaworska, J. S., Comber, M., Auer, C., & Van Leeuwen, C. J. (2003). Summary of a workshop on regulatory acceptance of (Q)SARs for human health and environmental endpoints. Environ Health Perspect, 111(10), 1358-1360. doi:10.1289/ehp.5757 Jiménez-González, M. L., Rebolledo-Chávez, J. P. F., Cruz-Ramírez, M., Antaño, R., Mendoza, A., Hosmane, N. S., Ortiz-Frade, L. (2022). Self-Assembled Monolayer of Monomercaptoundecahydro-closo-dodecaborate on a Polycrystalline Gold Surface. Molecules, 27(8), 2496. doi:10.3390/molecules27082496 Juretic, D., Kusic, H., Dionysiou, D. D., Rasulev, B., & Loncaric Bozic, A. (2014). Modeling of photooxidative degradation of aromatics in water matrix; combination of mechanistic and structural-relationship approach. Chemical Engineering Journal, 257, 229-241. doi:10.1016/j.cej.2014.07.053 Kamiya, Y., Omura, A., Hayasaka, R., Saito, R., Sano, I., Handa, K., Yamazaki, H. (2021). Prediction of permeability across intestinal cell monolayers for 219 disparate chemicals using in vitro experimental coefficients in a pH gradient system and in silico analyses by trivariate linear regressions and machine learning. Biochem Pharmacol, 192, 114749. doi:10.1016/j.bcp.2021.114749 Kiyama, R. (2023). Estrogenic flavonoids and their molecular mechanisms of action. The Journal of Nutritional Biochemistry, 114, 109250. doi:10.1016/j.jnutbio.2022.109250 Kolanczyk, R. C., Denny, J. S., Sheedy, B. R., Schmieder, P. K., & Tapper, M. A. (2019). Estrogenic activity of multicyclic aromatic hydrocarbons in rainbow trout (Oncorhynchus mykiss) in vitro assays. Aquat Toxicol, 207, 43-51. doi:10.1016/j.aquatox.2018.11.023 Landeros-Martínez, L. L., Glossman-Mitnik, D., & Flores-Holguín, N. (2018). Interaction of Tamoxifen Analogs With the Pocket Site of Some Hormone Receptors. A Molecular Docking and Density Functional Theory Study. Front Chem, 6, 293. doi:10.3389/fchem.2018.00293 Lawal, H. A., Uzairu, A., & Uba, S. (2020). QSAR, molecular docking, design, and pharmacokinetic analysis of 2-(4-fluorophenyl) imidazol-5-ones as anti-breast cancer drug compounds against MCF-7 cell line. J Bioenerg Biomembr, 52(6), 475-494. doi:10.1007/s10863-020-09858-0 Lee, S., & Barron, M. G. (2017). Structure-Based Understanding of Binding Affinity and Mode of Estrogen Receptor α Agonists and Antagonists. PLOS ONE, 12(1), e0169607. doi:10.1371/journal.pone.0169607 Li, C., Yang, X., Li, X., Chen, J., & Qiao, X. (2014). Development of a model for predicting hydroxyl radical reaction rate constants of organic chemicals at different temperatures. Chemosphere, 95, 613-618. doi:10.1016/j.chemosphere.2013.10.020 Lima Costa, A. H., Clemente, W. S., Bezerra, K. S., Lima Neto, J. X., Albuquerque, E. L., & Fulco, U. L. (2018). Computational biochemical investigation of the binding energy interactions between an estrogen receptor and its agonists. New Journal of Chemistry, 42(24), 19801-19810. doi:10.1039/C8NJ03521K Liu, M., Zhang, S., Du, S., Pang, S., Liu, X., & Zhang, H. (2020). A high throughput screening method for endocrine disrupting chemicals in tap water and milk samples based on estrogen receptor α and gold nanoparticles. Analytical Methods, 12(2), 200-204. doi:10.1039/C9AY02179E LoPachin, R. M., Gavin, T., DeCaprio, A., & Barber, D. S. (2012). Application of the Hard and Soft, Acids and Bases (HSAB) Theory to Toxicant–Target Interactions. Chemical Research in Toxicology, 25(2), 239-251. doi:10.1021/tx2003257 Lowe, C. N., Isaacs, K. K., McEachran, A., Grulke, C. M., Sobus, J. R., Ulrich, E. M., Williams, A. J. (2021). Predicting compound amenability with liquid chromatography-mass spectrometry to improve non-targeted analysis. Anal Bioanal Chem, 413(30), 7495-7508. doi:10.1007/s00216-021-03713-w Lundholm, C. E. (1997). DDE-induced eggshell thinning in birds: Effects of p,p'-DDE on the calcium and prostaglandin metabolism of the eggshell gland. Comparative Biochemistry and Physiology Part C: Pharmacology, Toxicology and Endocrinology, 118(2), 113-128. doi:10.1016/S0742-8413(97)00105-9 Luo, J., Xue, Z. Q., Liu, W. M., Wu, J. L., & Yang, Z. Q. (2006). Koopmans' theorem for large molecular systems within density functional theory. J Phys Chem A, 110(43), 12005-12009. doi:10.1021/jp063669m Lv, L., Dong, X., Lv, F., Zhao, W., Yu, Y., & Yang, W. (2017). Molecular cloning and characterization of an estrogen receptor gene in the marine polychaete Perinereis aibuhitensis. Comp Biochem Physiol B Biochem Mol Biol, 207, 15-21. doi:10.1016/j.cbpb.2017.02.001 M, M. R. (2022). Molecular docking analysis of phytochemicals with estrogen receptor alpha. Bioinformation, 18(8), 697-702. doi:10.6026/97320630018697 Mainali, K. (2020). Phenolic Compounds Contaminants in Water: A Glance. Current Trends in Civil & Structural Engineering, 4. doi:10.33552/CTCSE.2020.04.000593 Marinho, M. M., Almeida-Neto, F. W. Q., Marinho, E. M., da Silva, L. P., Menezes, R., Dos Santos, R. P., Martins, A. M. C. (2021). Quantum computational investigations and molecular docking studies on amentoflavone. Heliyon, 7(1), e06079. doi:10.1016/j.heliyon.2021.e06079 Maroni, G., Pallante, L., Di Benedetto, G., Deriu, M. A., Piga, D., & Grasso, G. (2022). Informed classification of sweeteners/bitterants compounds via explainable machine learning. Current Research in Food Science, 5, 2270-2280. doi:10.1016/j.crfs.2022.11.014 Martin, B. P., Brandon, C. J., Stewart, J. J., & Braun-Sand, S. B. (2015). Accuracy issues involved in modeling in vivo protein structures using PM7. Proteins, 83(8), 1427-1435. doi:10.1002/prot.24826 Mazurek, A. H., Szeleszczuk, Ł., Simonson, T., & Pisklak, D. M. (2020). Application of Various Molecular Modelling Methods in the Study of Estrogens and Xenoestrogens. Int J Mol Sci, 21(17). doi:10.3390/ijms21176411 Mazurek, A. H., Szeleszczuk, Ł., Simonson, T., & Pisklak, D. M. (2020). Application of Various Molecular Modelling Methods in the Study of Estrogens and Xenoestrogens. International Journal of Molecular Sciences, 21(17), 6411. doi:10.3390/ijms21176411 Mazzeo, D. E. C., Dombrowski, A., Oliveira, F. A., Levy, C. E., Oehlmann, J., & Marchi, M. R. R. (2023). Endocrine disrupting activity in sewage sludge: Screening method, microbial succession and cost-effective strategy for detoxification. J Environ Manage, 330, 117207. doi:10.1016/j.jenvman.2022.117207 Mekenyan, O. G., & Veith, G. D. (1993). Relationships between descriptors for hydrophobicity and soft electrophilicity in predicting toxicity. SAR QSAR Environ Res, 1(4), 335-344. doi:10.1080/10629369308029895 Mellanen, P., Petänen, T., Lehtimäki, J., Mäkelä, S., Bylund, G., Holmbom, B., Santti, R. (1996). Wood-derived estrogens: studies in vitro with breast cancer cell lines and in vivo in trout. Toxicol Appl Pharmacol, 136(2), 381-388. doi:10.1006/taap.1996.0046 Melnikov, F., Geohagen, B. C., Gavin, T., LoPachin, R. M., Anastas, P. T., Coish, P., & Herr, D. W. (2020). Application of the hard and soft, acids and bases (HSAB) theory as a method to predict cumulative neurotoxicity. Neurotoxicology, 79, 95-103. doi:10.1016/j.neuro.2020.04.009 Metcalfe, C. D., Bayen, S., Desrosiers, M., Muñoz, G., Sauvé, S., & Yargeau, V. (2022). An introduction to the sources, fate, occurrence and effects of endocrine disrupting chemicals released into the environment. Environmental Research, 207, 112658. doi:10.1016/j.envres.2021.112658 Nanjyo, S., Ohgane, K., Yoshioka, H., Makishima, M., Hashimoto, Y., & Noguchi-Yachide, T. (2019). Structure-activity relationship study of estrogen receptor down-regulators with a diphenylmethane skeleton. Bioorg Med Chem, 27(10), 1952-1961. doi:10.1016/j.bmc.2019.03.042 Nantasenamat, C., Isarankura-Na-Ayudhya, C., Naenna, T., & Prachayasittikul, V. (2008). Prediction of bond dissociation enthalpy of antioxidant phenols by support vector machine. J Mol Graph Model, 27(2), 188-196. doi:10.1016/j.jmgm.2008.04.005 Nitsche, C., & Otting, G. (2018). NMR studies of ligand binding. Curr Opin Struct Biol, 48, 16-22. doi:10.1016/j.sbi.2017.09.001 Olasupo, S. B., Uzairu, A., Adamu, G. S., & Uba, S. (2020). Computational Modeling and Pharmacokinetics/ADMET Study of Some Arylpiperazine Derivatives as Novel Antipsychotic Agents Targeting Depression. Chemistry Africa, 3(4), 979-988. doi:10.1007/s42250-020-00161-4 Olasupo, S. B., Uzairu, A., Shallangwa, G. A., & Uba, S. (2020). Profiling the antidepressant properties of phenyl piperidine derivatives as inhibitors of serotonin transporter (SERT) via cheminformatics modeling, molecular docking and ADMET predictions. Scientific African, 9, e00517. doi:10.1016/j.sciaf.2020.e00517 Pagadala, N. S., Syed, K., & Tuszynski, J. (2017). Software for molecular docking: a review. Biophys Rev, 9(2), 91-102. doi:10.1007/s12551-016-0247-1 Papa, E., Doucet, J. P., & Doucet-Panaye, A. (2016). Computational approaches for the prediction of the selective uptake of magnetofluorescent nanoparticles into human cells. RSC Advances, 6(73), 68806-68818. doi:10.1039/C6RA07898B Paul, Y., Parina, Singla, A., & Singh, B. (2013). In Silico Quantitative Structure Pharmacokinetic Relationship Modeling on Antidiabetic Drugs: Half-Life. International Journal of Chemical Sciences, 11, 177-185. Pearson, R. G., & Songstad, J. (1967). Application of the Principle of Hard and Soft Acids and Bases to Organic Chemistry. Journal of the American Chemical Society, 89(8), 1827-1836. doi:10.1021/ja00984a014 Pedretti, A., Mazzolari, A., Gervasoni, S., Fumagalli, L., & Vistoli, G. (2021). The VEGA suite of programs: an versatile platform for cheminformatics and drug design projects. Bioinformatics, 37(8), 1174-1175. doi:10.1093/bioinformatics/btaa774 Pilli, S. R., Banerjee, T., & Mohanty, K. (2015). HOMO–LUMO energy interactions between endocrine disrupting chemicals and ionic liquids using the density functional theory: Evaluation and comparison. Journal of Molecular Liquids, 207, 112-124. doi:10.1016/j.molliq.2015.03.019 Pinto, P. I. S., Andrade, A. R., Moreira, C., Zapater, C., Thorne, M. A. S., Santos, S., Power, D. M. (2019). Genistein and estradiol have common and specific impacts on the sea bass (Dicentrarchus labrax) skin-scale barrier. J Steroid Biochem Mol Biol, 195, 105448. doi:10.1016/j.jsbmb.2019.105448 Pironti, C., Ricciardi, M., Proto, A., Bianco, P. M., Montano, L., & Motta, O. (2021). Endocrine-Disrupting Compounds: An Overview on Their Occurrence in the Aquatic Environment and Human Exposure. Water, 13(10), 1347. doi:10.3390/w13101347 P.R, K. R., Mary, Y. S., Fernandez, A., S, A. P., Mary, Y. S., & Thomas, R. (2019). Single crystal XRD, DFT investigations and molecular docking study of 2- ((1,5-dimethyl-3-oxo-2-phenyl-2,3-dihydro-1H-pyrazol-4-yl)amino)naphthalene-1,4-dione as a potential anti- cancer lead molecule. Computational Biology and Chemistry, 78, 153-164. doi:10.1016/j.compbiolchem.2018.11.022 Priyadarshini, E., Parambil, A. M., Rajamani, P., Ponnusamy, V. K., & Chen, Y.-H. (2023). Exposure, toxicological mechanism of endocrine disrupting compounds and future direction of identification using nano-architectonics. Environmental Research, 225, 115577. doi:10.1016/j.envres.2023.115577 Przybyłek, M., Recki, Ł., Mroczyńska, K., Jeliński, T., & Cysewski, P. (2019). Experimental and theoretical solubility advantage screening of bi-component solid curcumin formulations. Journal of Drug Delivery Science and Technology, 50, 125-135. doi:10.1016/j.jddst.2019.01.023 Ramos, R. L., Moreira, V. R., Lebron, Y. A. R., Santos, A. V., Santos, L. V. S., & Amaral, M. C. S. (2021). Phenolic compounds seasonal occurrence and risk assessment in surface and treated waters in Minas Gerais-Brazil. Environ Pollut, 268(Pt A), 115782. doi:10.1016/j.envpol.2020.115782 Ranjbar Jafarabadi, A., Riyahi Bakhtiari, A., Mitra, S., Maisano, M., Cappello, T., & Jadot, C. (2019). First polychlorinated biphenyls (PCBs) monitoring in seawater, surface sediments and marine fish communities of the Persian Gulf: Distribution, levels, congener profile and health risk assessment. Environ Pollut, 253, 78-88. doi:10.1016/j.envpol.2019.07.023 Rearick, D. C., Fleischhacker, N. T., Kelly, M. M., Arnold, W. A., Novak, P. J., & Schoenfuss, H. L. (2014). Phytoestrogens in the environment, I: Occurrence and exposure effects on fathead minnows. Environmental Toxicology and Chemistry, 33(3), 553-559. doi:10.1002/etc.2461 Roy, R. K., & Saha, S. (2010). Studies of regioselectivity of large molecular systems using DFT based reactivity descriptors. Annual Reports Section "C" (Physical Chemistry), 106(0), 118-162. doi:10.1039/B811052M Sakkiah, S., Selvaraj, C., Guo, W., Liu, J., Ge, W., Patterson, T. A., & Hong, H. (2021). Elucidation of Agonist and Antagonist Dynamic Binding Patterns in ER-α by Integration of Molecular Docking, Molecular Dynamics Simulations and Quantum Mechanical Calculations. International Journal of Molecular Sciences, 22(17), 9371. doi:10.3390/ijms22179371 Sangeetha, S., Vimalkumar, K., & Loganathan, B. G. (2021). Environmental Contamination and Human Exposure to Select Endocrine-Disrupting Chemicals: A Review. Sustainable Chemistry, 2(2), 343-380. doi:10.3390/suschem2020020 Sarath Josh, M. K., Pradeep, S., Adarsh, V. K., Vijayalekshmi Amma, K. S., Sudha Devi, R., Balachandran, S., Benjamin, S. (2014). In silico evidences for the binding of phthalates onto human estrogen receptor α, β subtypes and human estrogen-related receptor γ. Molecular Simulation, 40(5), 408-417. doi:10.1080/08927022.2013.814131 Schmieder, P. K., Ankley, G., Mekenyan, O., Walker, J. D., & Bradbury, S. (2003). Quantitative structure-activity relationship models for prediction of estrogen receptor binding affinity of structurally diverse chemicals. Environmental Toxicology and Chemistry, 22(8), 1844-1854. doi:10.1897/01-345 Sellami, A., Montes, M., & Lagarde, N. (2021). Predicting Potential Endocrine Disrupting Chemicals Binding to Estrogen Receptor α (ERα) Using a Pipeline Combining Structure-Based and Ligand-Based in Silico Methods. Int J Mol Sci, 22(6). doi:10.3390/ijms22062846b Sellami, A., Réau, M., Montes, M., & Lagarde, N. (2022). Review of in silico studies dedicated to the nuclear receptor family: Therapeutic prospects and toxicological concerns. Front Endocrinol (Lausanne), 13, 986016. doi:10.3389/fendo.2022.986016 Semenza, J. C., Tolbert, P. E., Rubin, C. H., Guillette, L. J., Jr., & Jackson, R. J. (1997). Reproductive toxins and alligator abnormalities at Lake Apopka, Florida. Environ Health Perspect, 105(10), 1030-1032. doi:10.1289/ehp.971051030 Shanle, E. K., & Xu, W. (2011). Endocrine disrupting chemicals targeting estrogen receptor signaling: identification and mechanisms of action. Chem Res Toxicol, 24(1), 6-19. doi:10.1021/tx100231n Shen, C., Zhu, K., Ruan, J., Li, J., Wang, Y., Zhao, M., Zuo, Z. (2021). Screening of potential oestrogen receptor α agonists in pesticides via in silico, in vitro and in vivo methods. Environ Pollut, 270, 116015. doi:10.1016/j.envpol.2020.116015 Sheng, X., & Himo, F. (2023). The Quantum Chemical Cluster Approach in Biocatalysis. Accounts of Chemical Research, 56(8), 938-947. doi:10.1021/acs.accounts.2c00795 Sheng, X., Kazemi, M., Planas, F., & Himo, F. (2020). Modeling Enzymatic Enantioselectivity using Quantum Chemical Methodology. ACS Catalysis, 10(11), 6430-6449. doi:10.1021/acscatal.0c00983 Shiau, A. K., Barstad, D., Radek, J. T., Meyers, M. J., Nettles, K. W., Katzenellenbogen, B. S.,Greene, G. L. (2002). Structural characterization of a subtype-selective ligand reveals a novel mode of estrogen receptor antagonism. Nature Structural Biology, 9(5), 359-364. doi:10.1038/nsb787 Shylaja, R., Loganathan, C., Kabilan, S., Vijayakumar, T., & Meganathan, C. (2021). Synthesis and evaluation of the antagonistic activity of 3-acetyl-2H-benzo[g]chromen-2-one against mutant Y537S estrogen receptor alpha via E-Pharmacophore modeling, molecular docking, molecular dynamics, and in-vitro cytotoxicity studies. Journal of Molecular Structure, 1224, 129289. doi:10.1016/j.molstruc.2020.129289 Soliman, G., Abdul Aziz, M., Eissa, A., Elias, N., & Moustafa, M. (2020). Impacts of natural and experimental phenol pollution on the reproductive performance, vitellogenin synthesis and pathological alterations of male Oreochromis niloticus. Egyptian Journal of Aquatic Biology and Fisheries, 24, 479-495. doi:10.21608/ejabf.2020.101713 Spindola Vilela, C. L., Damasceno, T. L., Thomas, T., & Peixoto, R. S. (2022). Global qualitative and quantitative distribution of micropollutants in the deep sea. Environmental Pollution, 307, 119414. doi:10.1016/j.envpol.2022.119414 Sreenath Reddy, A., Mao, J., Vanitha, S., Badavath, V. N., Sivarama Krishna, L., Lavanya, M., & Jyothi kumar, M. V. (2019). Synthesis, spectral characterization, molecular docking studies, biological activity of (E)-2-((E)-3-(3,4,5-trimethoxyphenyl)allylidene) and (E)-N-phenyl 2-((E)-3-(3,4,5-trimethoxyphenyl)allylidene)hydrazinecarbothioamides and their Cu(II) complexes. Journal of Saudi Chemical Society, 23(7), 947-957. doi:10.1016/j.jscs.2019.04.003 Srivastava, A., Singh, H., Mishra, R., Dev, K., Tandon, P., & Maurya, R. (2017). Structural insights, protein-ligand interactions and spectroscopic characterization of isoformononetin. Journal of Molecular Structure, 1133, 479-491. doi:10.1016/j.molstruc.2016.12.043 Stephen, A., & Edache, E. (2016). APPLICATION OF GENETIC ALGORITHM-MULTIPLE LINEAR REGRESSION (GA-MLR) FOR PREDICTION OF ANTI-FUNGAL ACTIVITY. International Journal of Pharma Sciences and Research, 7 (4). Stewart, J. J. P. (2013). Optimization of parameters for semiempirical methods VI: more modifications to the NDDO approximations and re-optimization of parameters. Journal of Molecular Modeling, 19(1), 1-32. doi:10.1007/s00894-012-1667-x Stiefel, C., & Stintzing, F. (2023). Endocrine-active and endocrine-disrupting compounds in food – occurrence, formation and relevance. NFS Journal, 31, 57-92. doi:10.1016/j.nfs.2023.03.004 Streifer, M., & Gore, A. C. (2021). Chapter Three - Epigenetics, estrogenic endocrine-disrupting chemicals (EDCs), and the brain. In L. N. Vandenberg & J. L. Turgeon (Eds.), Advances in Pharmacology (Vol. 92, pp. 73-99): Academic Press. doi:10.1016/bs.apha.2021.03.006 Sun, Y., Chen, D., & Liu, C. (2002). Evaluation of the effectiveness of AM1 geometry used in calculating O–H bond dissociation enthalpy. Journal of Molecular Structure: THEOCHEM, 618(3), 181-189. doi:10.1016/S0166-1280(02)00523-7 Tan, H., Wang, X., Hong, H., Benfenati, E., Giesy, J. P., Gini, G. C., Shi, W. (2020). Structures of Endocrine-Disrupting Chemicals Determine Binding to and Activation of the Estrogen Receptor α and Androgen Receptor. Environmental Science & Technology, 54(18), 11424-11433. doi:10.1021/acs.est.0c02639 Toporova, L., & Balaguer, P. (2020). Nuclear receptors are the major targets of endocrine disrupting chemicals. Mol Cell Endocrinol, 502, 110665. doi:10.1016/j.mce.2019.110665 Torrent-Sucarrat, M., De Proft, F., Ayers, P. W., & Geerlings, P. (2010). On the applicability of local softness and hardness. Physical Chemistry Chemical Physics, 12(5), 1072-1080. doi:10.1039/B919471A Trott, O., & Olson, A. J. (2010). AutoDock Vina: improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading. J Comput Chem, 31(2), 455-461. doi:10.1002/jcc.21334 Tyler, C. R., Jobling, S., & Sumpter, J. P. (1998). Endocrine disruption in wildlife: a critical review of the evidence. Crit Rev Toxicol, 28(4), 319-361. doi:10.1080/10408449891344236 Umar, A. B., Uzairu, A., Shallangwa, G. A., & Uba, S. (2021). Ligand-based drug design and molecular docking simulation studies of some novel anticancer compounds on MALME-3M melanoma cell line. Egyptian Journal of Medical Human Genetics, 22(1), 6. doi:10.1186/s43042-020-00126-9 Uyanık, G. K., & Güler, N. (2013). A Study on Multiple Linear Regression Analysis. Procedia - Social and Behavioral Sciences, 106, 234-240. doi:10.1016/j.sbspro.2013.12.027 Uzzaman, M., Hasan, M. K., Mahmud, S., Yousuf, A., Islam, S., Uddin, M. N., & Barua, A. (2021). Physicochemical, spectral, molecular docking and ADMET studies of Bisphenol analogues; A computational approach. Informatics in Medicine Unlocked, 25, 100706. doi:10.1016/j.imu.2021.100706 Uzzaman, M., & Mahmud, T. (2020). Structural modification of aspirin to design a new potential cyclooxygenase (COX-2) inhibitors. In Silico Pharmacol, 8(1), 1. doi:10.1007/s40203-020-0053-0 Vandenberg, L. N., Najmi, A., & Mogus, J. P. (2020). Agrochemicals with estrogenic endocrine disrupting properties: Lessons Learned? Molecular and Cellular Endocrinology, 518, 110860. doi:10.1016/j.mce.2020.110860 Vedani, A., Dobler, M., & Lill, M. A. (2005). Combining Protein Modeling and 6D-QSAR. Simulating the Binding of Structurally Diverse Ligands to the Estrogen Receptor. Journal of Medicinal Chemistry, 48(11), 3700-3703. doi:10.1021/jm050185q Veith, G. D., & Mekenyan, O. G. (1993). A QSAR Approach for Estimating the Aquatic Toxicity of Soft Electrophiles [QSAR for Soft Electrophiles]. Quantitative Structure-Activity Relationships, 12(4), 349-356. doi:10.1002/qsar.19930120402 Verma, M. L., & Rani, V. (2021). Biosensors for toxic metals, polychlorinated biphenyls, biological oxygen demand, endocrine disruptors, hormones, dioxin, phenolic and organophosphorus compounds: a review. Environmental Chemistry Letters, 19(2), 1657-1666. doi:10.1007/s10311-020-01116-4 Villar, R., Gil, M. J., García, J. I., & Martínez-Merino, V. (2005). Are AM1 ligand-protein binding enthalpies good enough for use in the rational design of new drugs? J Comput Chem, 26(13), 1347-1358. doi:10.1002/jcc.20276 Wang, L.-F., & Zhang, H.-Y. (2005). A theoretical study on physicochemical properties of elsinochrome A in ground state. Journal of Molecular Structure: THEOCHEM, 726(1), 135-140. doi:10.1016/j.theochem.2005.04.002 Wang, L., Zhou, L., Liu, L., Yang, Y., & Zhao, Q. (2023). Comparative in vitro and in silico study on the estrogenic effects of 2,2-bis(4-chlorophenyl)ethanol, 4,4′-dichlorobenzophenone and DDT analogs. Science of The Total Environment, 876, 162734. doi:10.1016/j.scitotenv.2023.162734 Wang, X., Ha, D., Yoshitake, R., Chan, Y. S., Sadava, D., & Chen, S. (2021). Exploring the Biological Activity and Mechanism of Xenoestrogens and Phytoestrogens in Cancers: Emerging Methods and Concepts. Int J Mol Sci, 22(16). doi:10.3390/ijms22168798 Wei, F., Cheng, H., & Sang, N. (2022). Comprehensive assessment of estrogenic activities of parabens by in silico approach and in vitro assays. Science of The Total Environment, 845, 157194. doi:10.1016/j.scitotenv.2022.157194 Weis, K. E., Thompson, L. M., Streifer, M., Guardado, I., Flaws, J. A., Gore, A. C., & Raetzman, L. T. (2023). Pre- and postnatal developmental exposure to the polychlorinated biphenyl mixture aroclor 1221 alters female rat pituitary gonadotropins and estrogen receptor alpha levels. Reproductive Toxicology, 118, 108388. doi:10.1016/j.reprotox.2023.108388 Xue, Q., Liu, X., Liu, X.-C., Pan, W.-X., Fu, J.-J., & Zhang, A.-Q. (2019). The Effect of Structural Diversity on Ligand Specificity and Resulting Signaling Differences of Estrogen Receptor α. Chemical Research in Toxicology, 32(6), 1002-1013. doi:10.1021/acs.chemrestox.8b00338 Xue, Q., Liu, X., Russell, P., Li, J., Pan, W., Fu, J., & Zhang, A. (2022). Evaluation of the binding performance of flavonoids to estrogen receptor alpha by Autodock, Autodock Vina and Surflex-Dock. Ecotoxicol Environ Saf, 233, 113323. doi:10.1016/j.ecoenv.2022.113323 Yang, L., Sang, C., Wang, Y., Liu, W., Hao, W., Chang, J., & Li, J. (2021). Development of QSAR models for evaluating pesticide toxicity against Skeletonema costatum. Chemosphere, 285, 131456. doi:10.1016/j.chemosphere.2021.131456 Yang, X., Liu, H., & Kusko, R. (2019). Molecular Modeling Method Applications: Probing the Mechanism of Endocrine Disruptor Action. Computational Toxicology: Methodologies and Applications in Regulatory Science, 315-335. doi:10.1007/978-3-030-16443-0_16 Yap, C. W. (2011). PaDEL-descriptor: an open source software to calculate molecular descriptors and fingerprints. J Comput Chem, 32(7), 1466-1474. doi:10.1002/jcc.21707 Yaşar, P., Ayaz, G., User, S. D., Güpür, G., & Muyan, M. (2017). Molecular mechanism of estrogen-estrogen receptor signaling. Reprod Med Biol, 16(1), 4-20. doi:10.1002/rmb2.12006 Ye, H., Dudley, S. Z., & Shaw, I. C. (2018). Intimate estrogen receptor-α/ligand relationships signal biological activity. Toxicology, 408, 80-87. doi:10.1016/j.tox.2018.07.003 Ye, H., & Shaw, I. C. (2019). Food flavonoid ligand structure/estrogen receptor-α affinity relationships - toxicity or food functionality? Food Chem Toxicol, 129, 328-336. doi:10.1016/j.fct.2019.04.008 Yoshinouchi, Y., Shimizu, S., Lee, J. S., Hirano, M., Suzuki, K. T., Kim, E. Y., & Iwata, H. (2019). In vitro assessment of effects of persistent organic pollutants on the transactivation of estrogen receptor α and β (ERα and ERβ) from the Baikal seal (Pusa sibirica). Ecotoxicol Environ Saf, 181, 463-471. doi:10.1016/j.ecoenv.2019.06.033 Zhang, X., & Wu, C. (2022). In Silico, In Vitro, and In Vivo Evaluation of the Developmental Toxicity, Estrogenic Activity, and Mutagenicity of Four Natural Phenolic Flavonoids at Low Exposure Levels. ACS Omega, 7(6), 4757-4768. doi:10.1021/acsomega.1c04239 Zheng, R., Zhang, Y., Cheng, S., & Xiao, T. (2023). Environmental estrogens shape disease susceptibility. Int J Hyg Environ Health, 249, 114125. doi:10.1016/j.ijheh.2023.114125 Zhuang, S., Zhang, C., & Liu, W. (2014). Atomic insights into distinct hormonal activities of Bisphenol A analogues toward PPARγ and ERα receptors. Chem Res Toxicol, 27(10), 1769-1779. doi:10.1021/tx500232b | - |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/90153 | - |
| dc.description.abstract | 近年來，內分泌干擾物質(EDC)所引起的環境污染是一個重要的議題，由於內分泌系統對激素濃度的微小變化極其敏感，因此人們越來越關注可能模仿激素活性的環境污染物。全球有80%未經處理的工業廢水直接排出造成水污染及土壤污染，幾乎在每個生態系統中都能檢測到EDC。由於大多數的內分泌干擾物質作用機制仍然未知，測試所有使用過的化學品的作用是一項重要但也是昂貴且困難的任務。而且生物測定的可用性有限，體外和體內測定法存在實驗週期長、經濟成本高的缺點，因此不適用於大量化學品的常規篩查，反之迫切需要更合理的方法來幫助快速識別潛在有害的化學物質。
本研究採用PaDEL描述符和量子化學遺傳算法多重線性回歸（GA-MLR）方法對雌激素受體α(ERα)與異種雌激素(PCB、苯酚和DDT)和植物性雌激素的相對結合親和力（log RBA）進行預測。 優化模型的結果表明，異種雌激素和植物性雌激素皆受疏水相互作用的影響，不同的是異種雌激素主要是電子流動影響log RBA大小，而影響植物性雌激素的log RBA的主要因素則是氫鍵相互作用和分子的結構。透過全局的分子對接與局部的量子化學簇模型方法結果表明，氫鍵相互作用和疏水相互作用證實了內分泌干擾物化學質和ERα結合結構的穩定性。基於概念密度泛函理論計算的化學反應性描述符也表明電荷控制相互作用的結合機制通常會優於前沿控制相互作用的結合機制。 | zh_TW |
| dc.description.abstract | Environmental pollution caused by endocrine-disrupting substances (EDC) has been an essential topic recently. Since the endocrine system is susceptible to small changes in hormone concentrations, there has been increasing concern about environmental pollutants that may mimic hormone activity. 80% of the world's untreated industrial wastewater is directly discharged to cause water and soil pollution, and EDC can be detected in almost every ecosystem. Since the mechanism of action of most endocrine-disrupting substances remains unknown, testing the effects of all chemicals used is a necessary but also expensive and challenging task. Moreover, the limited availability of bioassays, in vitro and in vivo assays have the disadvantages of long experimental cycle and high economic cost, making them unsuitable for routine screening of many chemicals. In contrast, more rational methods are urgently needed to help rapidly identify potentially harmful chemicals.
In this study, PaDEL descriptors and quantum chemical genetic algorithm multiple linear regression (GA-MLR) methods were used to predict the relative binding affinities (log RBA) of estrogen receptor α (ERα) to xenoestrogens (PCB, phenol, and DDT) and phytoestrogens. The results of the optimized model show that both xenoestrogens and phytoestrogens are affected by hydrophobic interactions. The difference is that xenoestrogens mainly affect the size of log RBA by electron flow, while the main factors affecting log RBA of phytoestrogens are hydrogen bond interactions and molecular structures. Through the global molecular docking and local quantum chemical cluster modeling methods, the hydrogen bond interaction and hydrophobic interaction confirmed the stability of the endocrine disruptor chemical substance and the binding structure of ERα. Chemical reactivity descriptors based on conceptual density functional theory calculations suggest that binding mechanisms for charge-controlled interactions generally outperform those for front-controlled interactions. | en |
| dc.description.provenance | Submitted by admin ntu (admin@lib.ntu.edu.tw) on 2023-09-22T17:38:21Z No. of bitstreams: 0 | en |
| dc.description.provenance | Made available in DSpace on 2023-09-22T17:38:21Z (GMT). No. of bitstreams: 0 | en |
| dc.description.tableofcontents | Acknowledgement I
中文摘要 III Abstract IV List of Tables VIII List of Figures XI Nomenclature XIV Chapter 1. Introduction 1 1.1 Motivation 1 1.2 Research objectives 5 Chapter 2. Literature Review 9 2.1 Introduction of Estrogen Receptor Alpha (ERα) 9 2.2 Introduction of Endocrine Disrupting Chemicals (EDCs) 10 2.3 QSAR Models 14 2.4 Molecular Docking 15 2.5 Density Functional Theory (DFT) and Conceptual Density Functional Theory (CDFT) 16 2.6 Quantum Chemical Cluster Approach 18 Chapter 3. Material and Methods 19 3.1 Research framework 19 3.2 Dataset 21 3.3 Quantum Chemical Descriptors 21 3.4 PaDEL Descriptors 24 3.5 GA-MLR Method 24 3.6 Molecular Docking 26 3.7 Quantum Chemical Cluster Model Approach 27 Chapter 4. Result and Discussion 28 4.1 Development of the GA-MLR Models 28 4.1.1 PaDEL - Descriptor GA-MLR Model for Xenoestrogens 29 4.1.2 Quantum Chemical GA-MLR Model for Xenoestrogens 36 4.1.3 PaDEL - Descriptor GA-MLR Model for Phytoestrogens 42 4.1.4 Quantum Chemical GA-MLR Model for Phytoestrogens 48 4.1.5 PaDEL - Descriptor GA-MLR Model for Xenoestrogens and Phytoestrogens 54 4.1.6 Quantum Chemical GA-MLR Model for Xenoestrogens and Phytoestrogens 62 4.1.7 The Overall Interpretation of GA-MLR Models 70 4.2 Molecular Docking Simulation 74 4.2.1 The Interaction between Xenoestrogens and ERα 74 4.2.1.3 DDTs 88 4.2.2 The Interaction between Phytoestrogens and ERα 95 4.2.3 The Overall Interpretation of Molecular Docking Simulation 115 4.3 Quantum Chemical Cluster Model 116 4.3.1 The Results of Xenoestrogens with ERα 122 4.3.2 The Results of Phytoestrogens with ERα 132 4.3.3 The Overall Interpretation of Quantum Chemical Cluster Model 140 4.4 Conceptual Density Functional Theory 141 4.4.1 The Conceptual Density Functional Theory of Xenoestrogens 142 4.4.2 The Conceptual Density Functional Theory of Phytoestrogens 143 Chapter 5. Conclusion and Suggestion 146 5.1 Conclusion 146 5.2 Suggestion 147 References 148 Original Publications of This Dissertation 165 | - |
| dc.language.iso | en | - |
| dc.subject | 雌激素受體α | zh_TW |
| dc.subject | 內分泌干擾化學物質 | zh_TW |
| dc.subject | 遺傳算法多元線性回歸 | zh_TW |
| dc.subject | 分子對接 | zh_TW |
| dc.subject | 量子化學團簇模型 | zh_TW |
| dc.subject | 概念密度泛函理論 | zh_TW |
| dc.subject | Conceptual density functional theory | en |
| dc.subject | Molecular docking | en |
| dc.subject | Quantum chemical cluster model | en |
| dc.subject | Endocrine disrupting chemicals | en |
| dc.subject | Estrogen receptor α | en |
| dc.subject | Genetic algorithm multiple linear regression | en |
| dc.title | 雌激素受體α與內分泌干擾物結合相互作用的QSAR和量子化學研究 | zh_TW |
| dc.title | QSAR and Quantum Chemical Studies on the Binding Interaction of Estrogen Receptor Alpha with Endocrine Disrupting Chemicals | en |
| dc.type | Thesis | - |
| dc.date.schoolyear | 111-2 | - |
| dc.description.degree | 碩士 | - |
| dc.contributor.oralexamcommittee | 張家銘;童俊維 | zh_TW |
| dc.contributor.oralexamcommittee | Chia-Ming Chang;Chun-Wei Tung | en |
| dc.subject.keyword | 雌激素受體α,內分泌干擾化學物質,遺傳算法多元線性回歸,分子對接,量子化學團簇模型,概念密度泛函理論, | zh_TW |
| dc.subject.keyword | Estrogen receptor α,Endocrine disrupting chemicals,Genetic algorithm multiple linear regression,Molecular docking,Quantum chemical cluster model,Conceptual density functional theory, | en |
| dc.relation.page | 165 | - |
| dc.identifier.doi | 10.6342/NTU202304011 | - |
| dc.rights.note | 同意授權(全球公開) | - |
| dc.date.accepted | 2023-08-12 | - |
| dc.contributor.author-college | 工學院 | - |
| dc.contributor.author-dept | 環境工程學研究所 | - |
| dc.date.embargo-lift | 2028-08-10 | - |
| 顯示於系所單位： | 環境工程學研究所 | |
文件中的檔案：
| 檔案 | 大小 | 格式 | |
|---|---|---|---|
| ntu-111-2.pdf 此日期後於網路公開 2028-08-10 | 6.43 MB | Adobe PDF |
系統中的文件，除了特別指名其著作權條款之外，均受到著作權保護，並且保留所有的權利。