Login Register Cart 0
Review Article

最小拓扑差异的QSAR [s]:后现代主义的视角

卷 27, 期 1, 2020

页: [42 - 53] 页: 12

弟呕挨: 10.2174/0929867326666190704124857

价格: $65

摘要

在重新考虑的背景下的定量构效关系(构象)方法在经济层面,即经合组织的优化规则,目前审查展开Sterical最小的关键特性,蒙特卡罗和最小拓扑差异(MTD)方法,为定量开发治疗有机化合物的生物活性之间的关系(药物、农药等)和它们的结构。最初的最小空间差异(MSD)是由MTD方法的三维变体完成的，这是这里提到的最后一个，而验证和指导可行的QSAR方法的主要原则是由分析自动化MTD验证的，因此，在配体受体、空腔和壁的水平上扩大了对化学-生物相互作用的理解，为未来的适应性分子设计提供了真正的服务。

关键词: 最小拓扑差异(MTD)，最小立体差(MSD)，蒙特卡罗差分(MCD)，定量构效关系，药物设计，定量的治疗，经济合作与发展组织(经合组织)。

« Previous Next »
[1]
Fischer, E. Einfluss der Configuration auf die Wirkung der Enzyme. Chem. Ber., 1894, 27, 2985-2993.
[http://dx.doi.org/10.1002/cber.18940270364]
[2]
Simon, Z.; Chiriac, A.; Holban, Ş.; Ciubotariu, D. Mihalas, G.I.; Minimum steric difference. The MTD meth-od for QSAR studies. Letchworth, Hertfordshire, England: Research Studies Press; New York: Wiley, 1984.
[3]
Ciubotariu, D. Structure - Reactivity Relations within the class of Carbonic Acid Derivatives. PhD Thesis, Polytechnic Institute: Bucharest.1987.
[4]
Van de Waterbeemd, H. Quantitative approaches to structure-activity relationships in: The practice of medicinal chemistry; Wermuth, C.G., Ed.; Academic Press: London, 1996, pp. 367-386.
[5]
Ciubotariu, D.; Medeleanu, M.; Gogonea, V. Molecular Descriptors for QSPR/QSAR Studies; Diudea, M., Ed.; Nova Science, New York, 2000, pp. 281-388.
[6]
Niculescu-Duvăz, I.; Ciubotariu, D.; Simon, Z.; Voiculetz, N. QSAR (SAR) models and their use for carcinogenic potency prediction in: Modeling of Cancer Genesis and Prevention; Voiculetz, N.; Balaban, A.T.; Niculescu-Duvăz, I; Simon, Z., Ed.; CRC Press, 1991, pp. 157-214.
[7]
Comprehensive Medicinal Chemistry. Eds.; Pergamon Press: New York,, 1990, Vol. 4, pp. 1-31.
[8]
Hansch, C. The physicochemical approach to drug design and discovery (QSAR). Drug Dev. Res., 1981, 1, 267-309.
[http://dx.doi.org/10.1002/ddr.430010403]
[9]
Hansch, C. On the state of QSAR. Drug Inf. J., 1984, 18, 115-122.
[http://dx.doi.org/10.1177/009286158401800202]
[10]
Craig, P.N. QSAR - origins and present status: a historical perspective. Drug Inf. J., 1984, 18, 123-130.
[http://dx.doi.org/10.1177/009286158401800203]
[11]
Hansch, C. Quantitative structure - activity relationships and the unnamed science. Acc. Chem. Res., 1993, 26, 147-153.
[http://dx.doi.org/10.1021/ar00028a003]
[12]
Oprea, T.I.; Ciubotariu, D.; Sulea, T. şi Simon, Z. Comparison of the minimal steric difference (MTD) and comparative molecular field analysis (CoMFA) methods of binding of steroids to carrier proteins, Quant. Struct.-. Act. Relat., 1993, 12, 21-26.
[http://dx.doi.org/10.1002/qsar.19930120104]
[13]
Ciubotariu, D.; Derertey, E.; Oprea, T.I.; Sulea, T.; Simon, Z.; Kurunczi, L.; Chiriac, A. Multiconformational minimal steric difference. structure-acetylcholinesterase hydrolysis rates relations for acetic acid esters, Quant. Struct.-. Act. Relat., 1993, 12, 367-372.
[http://dx.doi.org/10.1002/qsar.19930120404]
[14]
Balaban, A.; Chiriac, A.; Moţoc, I.; Simon, Z. Steric fit in quantitative structure-activity relationships; Springer Verlag: New York, 1980.
[http://dx.doi.org/10.1007/978-3-642-48316-5]
[15]
Goldstein, A.; Aronov, L.; Kalman, S.M. Principle of drug action; John Wiley: New York, 1994, p. 25.
[16]
Jeong, H.; Tombor, B.; Albert, R.; Oltvai, Z.N.; Barabási, A-L. The large-scale organization of metabolic networks. Nature, 2000, 407(6804), 651-654.
[http://dx.doi.org/10.1038/35036627] [PMID: 11034217]
[17]
Albert, R.; Jeong, H.; Barabasi, A-L. Error and attack tolerance of complex networks. Nature, 2000, 406(6794), 378-382.
[http://dx.doi.org/10.1038/35019019] [PMID: 10935628]
[18]
Jeong, H.; Mason, S.P.; Barabási, A-L.; Oltvai, Z.N. Lethality and centrality in protein networks. Nature, 2001, 411(6833), 41-42.
[http://dx.doi.org/10.1038/35075138] [PMID: 11333967]
[19]
Barabási, A-L.; Oltvai, Z.N. Network biology: understanding the cell’s functional organization. Nat. Rev. Genet., 2004, 5(2), 101-113.
[http://dx.doi.org/10.1038/nrg1272] [PMID: 14735121]
[20]
Ravasz, E.; Somera, A.L.; Mongru, D.A.; Oltvai, Z.N.; Barabási, A-L. Hierarchical organization of modularity in metabolic networks. Science, 2002, 297(5586), 1551-1555.
[http://dx.doi.org/10.1126/science.1073374] [PMID: 12202830]
[21]
Kovács, I.A.; Barabási, A-L. Network science: Destruction perfected. Nature, 2015, 524(7563), 38-39.
[http://dx.doi.org/10.1038/524038a] [PMID: 26245576]
[22]
Guidance document on the validation of (Quantitative) structure-activity relationship [(Q)SAR] models, series on testing and assessment. 69, OECD, Paris, 2007, pp. 154. Available At: . https://www.oecd.org/chemicalsafety/risk-assessment/validationofqsarmodels.htm (Accessed Date: 2 June,2019)
[23]
Putz, M.V.; Lacrămă, A-M. Introducing spectral structure activity relationship (S-SAR) Analysis. Application to Ecotoxicology. Int. J. Mol. Sci., 2007, 8(5), 363-391.
[http://dx.doi.org/10.3390/i8050363]
[24]
Putz, M.V.; Putz, A.M.; Lazea, M.; Ienciu, L.; Chiriac, A. Quantum-SAR extension of the spectral-SAR algorithm: application to polyphenolic anticancer bioactivity. Int. J. Mol. Sci., 2009, 10(3), 1193-1214.
[http://dx.doi.org/10.3390/ijms10031193] [PMID: 19399244]
[25]
Putz, M.V. Residual-QSAR. Implications for genotoxic carcinogenesis. Chem. Cent. J., 2011, 5(29), 29.
[http://dx.doi.org/10.1186/1752-153X-5-29] [PMID: 21668999]
[26]
Putz, M.V.; Ionaşcu, C.; Putz, A.M.; Ostafe, V. Alert-QSAR. Implications for electrophilic theory of chemical carcinogenesis. Int. J. Mol. Sci., 2011, 12(8), 5098-5134.
[http://dx.doi.org/10.3390/ijms12085098] [PMID: 21954348]
[27]
Putz, M.V.; Lazea, M.; Putz, A.M.; Duda-Seiman, C. Introducing catastrophe-QSAR. Application on modeling molecular mechanisms of pyridinone derivative-type HIV non-nucleoside reverse transcriptase inhibitors. Int. J. Mol. Sci., 2011, 12(12), 9533-9569.
[http://dx.doi.org/10.3390/ijms12129533] [PMID: 22272148]
[28]
Putz, M.V.; Dudaș, N.A. Variational principles for mechanistic quantitative structure–activity relationship (QSAR) studies: application on uracil derivatives’ anti-HIV action. Struct. Chem., 2013, 24(6), 1873-1893.
[http://dx.doi.org/10.1007/s11224-013-0249-6]
[29]
Putz, M.V.; Dudaş, N.A. Determining chemical reactivity driving biological activity from SMILES transformations: the bonding mechanism of anti-HIV pyrimidines. Molecules, 2013, 18(8), 9061-9116.
[http://dx.doi.org/10.3390/molecules18089061] [PMID: 23903183]
[30]
Putz, M.V.; Ori, O.; Cataldo, F.; Putz, A.M. Parabolic reactivity “coloring” molecular topology: Application to carcinogenic PAHs. Curr. Org. Chem., 2013, 17(23), 2816-2830.
[http://dx.doi.org/10.2174/13852728113179990128]
[31]
Tudoran, M.A.; Putz, M.V. Molecular graph theory: from adjacency information to colored topology by chemical reactivity. Curr. Org. Chem., 2015, 19(4), 359-386.
[http://dx.doi.org/10.2174/1385272819666141216232941]
[32]
Putz, M.V.; Duda-Seiman, C.; Duda-Seiman, D.; Putz, A.M.; Alexandrescu, I.; Mernea, M.; Avram, S. Chemical structure-biological activity models for pharmacophores’ 3D-Interactions. Int. J. Mol. Sci., 2016, 17(7), 1087.
[http://dx.doi.org/10.3390/ijms17071087] [PMID: 27399692]
[33]
Putz, M.V. A Fully Integrated Approach: Vol V. Quantum Structure-Activity Relationship (Qu-SAR) In: Apple Academic; Press & CRC Press, 2016; Vol. 5, p. 622.

Rights & Permissions Print Cite
Article Metrics
26
1
© 2024 Bentham Science Publishers | Privacy Policy