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1.

Multi-Descriptor Read Across (MuDRA): A Simple and Transparent Approach for Developing Accurate Quantitative Structure-Activity Relationship Models.

Alves, Vinicius M; Golbraikh, Alexander; Capuzzi, Stephen J; Liu, Kammy; Lam, Wai In; Korn, Daniel Robert; Pozefsky, Diane; Andrade, Carolina Horta; Muratov, Eugene N; Tropsha, Alexander.

J Chem Inf Model; 58(6): 1214-1223, 2018 06 25.

Artículo en Inglés | MEDLINE | ID: mdl-29809005

2.

Data set modelability by QSAR.

Golbraikh, Alexander; Muratov, Eugene; Fourches, Denis; Tropsha, Alexander.

J Chem Inf Model; 54(1): 1-4, 2014 Jan 27.

Artículo en Inglés | MEDLINE | ID: mdl-24251851

3.

Application of quantitative structure-activity relationship models of 5-HT1A receptor binding to virtual screening identifies novel and potent 5-HT1A ligands.

Luo, Man; Wang, Xiang Simon; Roth, Bryan L; Golbraikh, Alexander; Tropsha, Alexander.

J Chem Inf Model; 54(2): 634-47, 2014 Feb 24.

Artículo en Inglés | MEDLINE | ID: mdl-24410373

4.

Integrative chemical-biological read-across approach for chemical hazard classification.

Low, Yen; Sedykh, Alexander; Fourches, Denis; Golbraikh, Alexander; Whelan, Maurice; Rusyn, Ivan; Tropsha, Alexander.

Chem Res Toxicol; 26(8): 1199-208, 2013 Aug 19.

Artículo en Inglés | MEDLINE | ID: mdl-23848138

5.

Discovery of novel antimalarial compounds enabled by QSAR-based virtual screening.

Zhang, Liying; Fourches, Denis; Sedykh, Alexander; Zhu, Hao; Golbraikh, Alexander; Ekins, Sean; Clark, Julie; Connelly, Michele C; Sigal, Martina; Hodges, Dena; Guiguemde, Armand; Guy, R Kiplin; Tropsha, Alexander.

J Chem Inf Model; 53(2): 475-92, 2013 Feb 25.

Artículo en Inglés | MEDLINE | ID: mdl-23252936

6.

Does rational selection of training and test sets improve the outcome of QSAR modeling?

Martin, Todd M; Harten, Paul; Young, Douglas M; Muratov, Eugene N; Golbraikh, Alexander; Zhu, Hao; Tropsha, Alexander.

J Chem Inf Model; 52(10): 2570-8, 2012 Oct 22.

Artículo en Inglés | MEDLINE | ID: mdl-23030316

7.

Value of p-Value.

Golbraikh, Alexander.

Mol Inform; 38(8-9): e1800152, 2019 08.

Artículo en Inglés | MEDLINE | ID: mdl-31188542

8.

Development of quantitative structure-binding affinity relationship models based on novel geometrical chemical descriptors of the protein-ligand interfaces.

Zhang, Shuxing; Golbraikh, Alexander; Tropsha, Alexander.

J Med Chem; 49(9): 2713-24, 2006 May 04.

Artículo en Inglés | MEDLINE | ID: mdl-16640331

9.

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

Shen, Min; Xiao, Yunde; Golbraikh, Alexander; Gombar, Vijay K; Tropsha, Alexander.

J Med Chem; 46(14): 3013-20, 2003 Jul 03.

Artículo en Inglés | MEDLINE | ID: mdl-12825940

10.

Antitumor agents. 213. Modeling of epipodophyllotoxin derivatives using variable selection k nearest neighbor QSAR method.

Xiao, Zhiyan; Xiao, Yun-De; Feng, Jun; Golbraikh, Alexander; Tropsha, Alexander; Lee, Kuo-Hsiung.

J Med Chem; 45(11): 2294-309, 2002 May 23.

Artículo en Inglés | MEDLINE | ID: mdl-12014968

11.

Application of predictive QSAR models to database mining: identification and experimental validation of novel anticonvulsant compounds.

Shen, Min; Béguin, Cécile; Golbraikh, Alexander; Stables, James P; Kohn, Harold; Tropsha, Alexander.

J Med Chem; 47(9): 2356-64, 2004 Apr 22.

Artículo en Inglés | MEDLINE | ID: mdl-15084134

12.

Quantitative structure-activity relationship analysis of functionalized amino acid anticonvulsant agents using k nearest neighbor and simulated annealing PLS methods.

Shen, Min; LeTiran, Arnaud; Xiao, Yunde; Golbraikh, Alexander; Kohn, Harold; Tropsha, Alexander.

J Med Chem; 45(13): 2811-23, 2002 Jun 20.

Artículo en Inglés | MEDLINE | ID: mdl-12061883

13.

Beware of q2!

Golbraikh, Alexander; Tropsha, Alexander.

J Mol Graph Model; 20(4): 269-76, 2002 Jan.

Artículo en Inglés | MEDLINE | ID: mdl-11858635

14.

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

Cern, Ahuva; Golbraikh, Alexander; Sedykh, Aleck; Tropsha, Alexander; Barenholz, Yechezkel; Goldblum, Amiram.

J Control Release; 160(2): 147-57, 2012 Jun 10.

Artículo en Inglés | MEDLINE | ID: mdl-22154932

15.

Development, validation, and use of quantitative structure-activity relationship models of 5-hydroxytryptamine (2B) receptor ligands to identify novel receptor binders and putative valvulopathic compounds among common drugs.

Hajjo, Rima; Grulke, Christopher M; Golbraikh, Alexander; Setola, Vincent; Huang, Xi-Ping; Roth, Bryan L; Tropsha, Alexander.

J Med Chem; 53(21): 7573-86, 2010 Nov 11.

Artículo en Inglés | MEDLINE | ID: mdl-20958049

16.

A novel two-step hierarchical quantitative structure-activity relationship modeling work flow for predicting acute toxicity of chemicals in rodents.

Zhu, Hao; Ye, Lin; Richard, Ann; Golbraikh, Alexander; Wright, Fred A; Rusyn, Ivan; Tropsha, Alexander.

Environ Health Perspect; 117(8): 1257-64, 2009 Aug.

Artículo en Inglés | MEDLINE | ID: mdl-19672406

17.

QSAR modeling of the blood-brain barrier permeability for diverse organic compounds.

Zhang, Liying; Zhu, Hao; Oprea, Tudor I; Golbraikh, Alexander; Tropsha, Alexander.

Pharm Res; 25(8): 1902-14, 2008 Aug.

Artículo en Inglés | MEDLINE | ID: mdl-18553217

18.

Combinatorial QSAR modeling of specificity and subtype selectivity of ligands binding to serotonin receptors 5HT1E and 5HT1F.

Wang, Xiang S; Tang, Hao; Golbraikh, Alexander; Tropsha, Alexander.

J Chem Inf Model; 48(5): 997-1013, 2008 May.

Artículo en Inglés | MEDLINE | ID: mdl-18470978

19.

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

Hsieh, Jui-Hua; Wang, Xiang S; Teotico, Denise; Golbraikh, Alexander; Tropsha, Alexander.

J Comput Aided Mol Des; 22(9): 593-609, 2008 Sep.

Artículo en Inglés | MEDLINE | ID: mdl-18338225

20.

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

Tropsha, Alexander; Golbraikh, Alexander.

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

Artículo en Inglés | MEDLINE | ID: mdl-18220786

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