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Items: 1 to 20 of 118

1.

QSPR modelling with the topological substructural molecular design approach: beta-cyclodextrin complexation.

Pérez-Garrido A, Helguera AM, Cordeiro MN, Escudero AG.

J Pharm Sci. 2009 Dec;98(12):4557-76. doi: 10.1002/jps.21747.

PMID:
19504577
2.

Convenient QSAR model for predicting the complexation of structurally diverse compounds with beta-cyclodextrins.

Pérez-Garrido A, Morales Helguera A, Abellán Guillén A, Cordeiro MN, Garrido Escudero A.

Bioorg Med Chem. 2009 Jan 15;17(2):896-904. doi: 10.1016/j.bmc.2008.11.040. Epub 2008 Nov 24.

PMID:
19056282
3.

Development of machine learning models of β-cyclodextrin and sulfobutylether-β-cyclodextrin complexation free energies.

Merzlikine A, Abramov YA, Kowsz SJ, Thomas VH, Mano T.

Int J Pharm. 2011 Oct 14;418(2):207-16. doi: 10.1016/j.ijpharm.2011.03.065. Epub 2011 Apr 8.

PMID:
21497190
4.

In silico prediction of the β-cyclodextrin complexation based on Monte Carlo method.

Veselinović AM, Veselinović JB, Toropov AA, Toropova AP, Nikolić GM.

Int J Pharm. 2015 Nov 10;495(1):404-9. doi: 10.1016/j.ijpharm.2015.08.078. Epub 2015 Aug 28.

PMID:
26320546
5.

Combination of 2D-, 3D-connectivity and quantum chemical descriptors in QSPR. Complexation of alpha- and beta-cyclodextrin with benzene derivatives.

Estrada E, Perdomo-López I, Torres-Labandeira JJ.

J Chem Inf Comput Sci. 2001 Nov-Dec;41(6):1561-8.

PMID:
11749583
6.

Aggregation of cyclodextrins as an important factor to determine their complexation behavior.

Bikádi Z, Kurdi R, Balogh S, Szemán J, Hazai E.

Chem Biodivers. 2006 Nov;3(11):1266-78.

PMID:
17193241
7.

Chiral recognition of aromatic compounds by beta-cyclodextrin based on bimodal complexation.

Cai W, Yu Y, Shao X.

J Mol Model. 2005 Jun;11(3):186-93. Epub 2005 May 18.

PMID:
15900481
8.

Enhanced solubility and antibacterial activity of lipophilic fluoro-substituted N-benzoyl-2-aminobenzothiazoles by complexation with β-cyclodextrins.

Trapani A, De Laurentis N, Armenise D, Carrieri A, Defrenza I, Rosato A, Mandracchia D, Tripodo G, Salomone A, Capriati V, Franchini C, Corbo F.

Int J Pharm. 2016 Jan 30;497(1-2):18-22. doi: 10.1016/j.ijpharm.2015.11.024. Epub 2015 Nov 28.

PMID:
26611670
9.

New QSPR study for the prediction of aqueous solubility of drug-like compounds.

Duchowicz PR, Talevi A, Bruno-Blanch LE, Castro EA.

Bioorg Med Chem. 2008 Sep 1;16(17):7944-55. doi: 10.1016/j.bmc.2008.07.067. Epub 2008 Jul 29.

PMID:
18701302
10.

Modeling the effect of selected cyclodextrins on nifedipine solubility.

Agatonovic-Kustrin S, Morton DW, Worthington MS, Glass BD.

Curr Drug Discov Technol. 2011 Jun;8(2):146-54.

PMID:
21091429
11.

Predicting complexation thermodynamic parameters of β-cyclodextrin with chiral guests by using swarm intelligence and support vector machines.

Prakasvudhisarn C, Wolschann P, Lawtrakul L.

Int J Mol Sci. 2009 May 14;10(5):2107-21. doi: 10.3390/ijms10052107.

12.

Empirical, thermodynamic and quantum-chemical investigations of inclusion complexation between flavanones and (2-hydroxypropyl)-cyclodextrins.

Liu B, Li W, Nguyen TA, Zhao J.

Food Chem. 2012 Sep 15;134(2):926-32. doi: 10.1016/j.foodchem.2012.02.207. Epub 2012 Mar 8.

PMID:
23107709
13.

Development of improved empirical models for estimating the binding constant of a beta-cyclodextrin inclusion complex.

Chari R, Qureshi F, Moschera J, Tarantino R, Kalonia D.

Pharm Res. 2009 Jan;26(1):161-71. doi: 10.1007/s11095-008-9733-x. Epub 2008 Oct 9.

PMID:
18843449
14.

A topological substructural approach applied to the computational prediction of rodent carcinogenicity.

Helguera AM, Cabrera Pérez MA, González MP, Ruiz RM, González Díaz H.

Bioorg Med Chem. 2005 Apr 1;13(7):2477-88.

PMID:
15755650
15.

QSPR model for bioconcentration factors of nonpolar organic compounds using molecular electronegativity distance vector descriptors.

Qin LT, Liu SS, Liu HL.

Mol Divers. 2010 Feb;14(1):67-80. doi: 10.1007/s11030-009-9145-9. Epub 2009 Apr 15.

PMID:
19367470
16.

Molecular van der Waals space and topological indices from the distance matrix.

Ciubotariu D, Medeleanu M, Vlaia V, Olariu T, Ciubotariu C, Dragos D, Corina S.

Molecules. 2004 Dec 31;9(12):1053-78. Review.

17.

Elucidation of specific aspects of dielectric constants of conjugated organic compounds: a QSPR approach.

Lee A, Kim D, Kim KH, Choi SH, Choi K, Jung DH.

J Mol Model. 2012 Jan;18(1):251-6. doi: 10.1007/s00894-011-1067-7. Epub 2011 Apr 27.

PMID:
21523536
18.

Estimation of aqueous solubility of organic compounds with QSPR approach.

Gao H, Shanmugasundaram V, Lee P.

Pharm Res. 2002 Apr;19(4):497-503.

PMID:
12033386
19.

QSPR modelling of dielectric constants of π-conjugated organic compounds by means of the CORAL software.

Achary PG.

SAR QSAR Environ Res. 2014;25(6):507-26. doi: 10.1080/1062936X.2014.899267. Epub 2014 Apr 9.

PMID:
24716837
20.

Highly diverse, massive organic data as explored by a composite QSPR strategy: an advanced study of boiling point.

Ivanova AA, Ivanov AA, Oliferenko AA, Palyulin VA, Zefirov NS.

SAR QSAR Environ Res. 2005 Jun;16(3):231-46.

PMID:
15804811

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