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Items: 1 to 20 of 111

1.

Computer-aided design of liposomal drugs: In silico prediction and experimental validation of drug candidates for liposomal remote loading.

Cern A, Barenholz Y, Tropsha A, Goldblum A.

J Control Release. 2014 Jan 10;173:125-31. doi: 10.1016/j.jconrel.2013.10.029. Epub 2013 Oct 31.

2.

Quantitative structure-property relationship modeling of remote liposome loading of drugs.

Cern A, Golbraikh A, Sedykh A, Tropsha A, Barenholz Y, Goldblum A.

J Control Release. 2012 Jun 10;160(2):147-57. doi: 10.1016/j.jconrel.2011.11.029. Epub 2011 Dec 1.

3.

Liposome drugs' loading efficiency: a working model based on loading conditions and drug's physicochemical properties.

Zucker D, Marcus D, Barenholz Y, Goldblum A.

J Control Release. 2009 Oct 1;139(1):73-80. doi: 10.1016/j.jconrel.2009.05.036. Epub 2009 Jun 7.

PMID:
19508880
4.

Development and validation of k-nearest-neighbor QSPR models of metabolic stability of drug candidates.

Shen M, Xiao Y, Golbraikh A, Gombar VK, Tropsha A.

J Med Chem. 2003 Jul 3;46(14):3013-20.

PMID:
12825940
5.

Ligand-based virtual screening and in silico design of new antimalarial compounds using nonstochastic and stochastic total and atom-type quadratic maps.

Marrero-Ponce Y, Iyarreta-Veitía M, Montero-Torres A, Romero-Zaldivar C, Brandt CA, Avila PE, Kirchgatter K, Machado Y.

J Chem Inf Model. 2005 Jul-Aug;45(4):1082-100.

PMID:
16045304
6.

Predictive QSAR modeling workflow, model applicability domains, and virtual screening.

Tropsha A, Golbraikh A.

Curr Pharm Des. 2007;13(34):3494-504. Review.

PMID:
18220786
7.

Intravitreal clearance and volume of distribution of compounds in rabbits: In silico prediction and pharmacokinetic simulations for drug development.

del Amo EM, Vellonen KS, Kidron H, Urtti A.

Eur J Pharm Biopharm. 2015 Sep;95(Pt B):215-26. doi: 10.1016/j.ejpb.2015.01.003. Epub 2015 Jan 17.

PMID:
25603198
8.

Bond-based global and local (bond, group and bond-type) quadratic indices and their applications to computer-aided molecular design. 1. QSPR studies of diverse sets of organic chemicals.

Marrero-Ponce Y, Torrens F, Alvarado YJ, Rotondo R.

J Comput Aided Mol Des. 2006 Oct-Nov;20(10-11):685-701. Epub 2006 Nov 25.

PMID:
17186417
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.

Liposomal drugs dispersed in hydrogels. Effect of liposome, drug and gel properties on drug release kinetics.

Mourtas S, Fotopoulou S, Duraj S, Sfika V, Tsakiroglou C, Antimisiaris SG.

Colloids Surf B Biointerfaces. 2007 Apr 1;55(2):212-21. Epub 2006 Dec 17.

PMID:
17223020
11.

General theory for multiple input-output perturbations in complex molecular systems. 1. Linear QSPR electronegativity models in physical, organic, and medicinal chemistry.

González-Díaz H, Arrasate S, Gómez-SanJuan A, Sotomayor N, Lete E, Besada-Porto L, Ruso JM.

Curr Top Med Chem. 2013;13(14):1713-41. Review.

PMID:
23889050
12.

Liposomal drug delivery systems: from concept to clinical applications.

Allen TM, Cullis PR.

Adv Drug Deliv Rev. 2013 Jan;65(1):36-48. doi: 10.1016/j.addr.2012.09.037. Epub 2012 Oct 1. Review.

PMID:
23036225
13.

QSPR models for the prediction of apparent volume of distribution.

Ghafourian T, Barzegar-Jalali M, Dastmalchi S, Khavari-Khorasani T, Hakimiha N, Nokhodchi A.

Int J Pharm. 2006 Aug 17;319(1-2):82-97. Epub 2006 Apr 7.

PMID:
16698204
14.

Determination of partitioning of drug molecules using immobilized liposome chromatography and chemometrics methods.

Noorizadeh H, Farmany A.

Drug Test Anal. 2012 Feb;4(2):151-7. doi: 10.1002/dta.262. Epub 2011 Mar 25.

PMID:
21438160
15.

TOMOCOMD-CARDD, a novel approach for computer-aided 'rational' drug design: I. Theoretical and experimental assessment of a promising method for computational screening and in silico design of new anthelmintic compounds.

Marrero-Ponce Y, Castillo-Garit JA, Olazabal E, Serrano HS, Morales A, Castañedo N, Ibarra-Velarde F, Huesca-Guillen A, Jorge E, del Valle A, Torrens F, Castro EA.

J Comput Aided Mol Des. 2004 Oct;18(10):615-34.

PMID:
15849993
16.

Differentiation of AmpC beta-lactamase binders vs. decoys using classification kNN QSAR modeling and application of the QSAR classifier to virtual screening.

Hsieh JH, Wang XS, Teotico D, Golbraikh A, Tropsha A.

J Comput Aided Mol Des. 2008 Sep;22(9):593-609. doi: 10.1007/s10822-008-9199-2. Epub 2008 Mar 13.

PMID:
18338225
17.

From drug target to leads--sketching a physicochemical pathway for lead molecule design in silico.

Shaikh SA, Jain T, Sandhu G, Latha N, Jayaram B.

Curr Pharm Des. 2007;13(34):3454-70. Review.

PMID:
18220783
18.

Toward in silico prediction of glass-forming ability from molecular structure alone: a screening tool in early drug development.

Mahlin D, Ponnambalam S, Höckerfelt MH, Bergström CA.

Mol Pharm. 2011 Apr 4;8(2):498-506. doi: 10.1021/mp100339c. Epub 2011 Mar 18.

PMID:
21344945
19.

QSPR models for computer-aided drug design in microbiology, parasitology, and pharmacology.

Gonzalez-Diaz H.

Curr Comput Aided Drug Des. 2011 Dec;7(4):228-30. No abstract available.

PMID:
22050675
20.

Design, synthesis and experimental validation of novel potential chemopreventive agents using random forest and support vector machine binary classifiers.

Sprague B, Shi Q, Kim MT, Zhang L, Sedykh A, Ichiishi E, Tokuda H, Lee KH, Zhu H.

J Comput Aided Mol Des. 2014 Jun;28(6):631-46. doi: 10.1007/s10822-014-9748-9. Epub 2014 May 20.

PMID:
24840854
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