177 related articles for article (PubMed ID: 18686944)
21. Characterization of activity landscapes using 2D and 3D similarity methods: consensus activity cliffs.
Medina-Franco JL; Martínez-Mayorga K; Bender A; Marín RM; Giulianotti MA; Pinilla C; Houghten RA
J Chem Inf Model; 2009 Feb; 49(2):477-91. PubMed ID: 19434846
[TBL] [Abstract][Full Text] [Related]
22. 2D-3D migration of large chemical inventories with conformational multiplication. Application of the genetic algorithm.
Mekenyan O; Pavlov T; Grancharov V; Todorov M; Schmieder P; Veith G
J Chem Inf Model; 2005; 45(2):283-92. PubMed ID: 15807489
[TBL] [Abstract][Full Text] [Related]
23. 3D-MEDNEs: an alternative "in silico" technique for chemical research in toxicology. 2. quantitative proteome-toxicity relationships (QPTR) based on mass spectrum spiral entropy.
Cruz-Monteagudo M; González-Díaz H; Borges F; Dominguez ER; Cordeiro MN
Chem Res Toxicol; 2008 Mar; 21(3):619-32. PubMed ID: 18257557
[TBL] [Abstract][Full Text] [Related]
24. Synthesis, anti-tuberculosis activity and 3D-QSAR study of amino acid conjugates of 4-(adamantan-1-yl) group containing quinolines.
Nayyar A; Patel SR; Shaikh M; Coutinho E; Jain R
Eur J Med Chem; 2009 May; 44(5):2017-29. PubMed ID: 19022537
[TBL] [Abstract][Full Text] [Related]
25. Unified QSAR and network-based computational chemistry approach to antimicrobials, part 1: multispecies activity models for antifungals.
González-Díaz H; Prado-Prado FJ
J Comput Chem; 2008 Mar; 29(4):656-67. PubMed ID: 17999385
[TBL] [Abstract][Full Text] [Related]
26. Quantitative structure-activity relationship models of chemical transformations from matched pairs analyses.
Beck JM; Springer C
J Chem Inf Model; 2014 Apr; 54(4):1226-34. PubMed ID: 24605924
[TBL] [Abstract][Full Text] [Related]
27. Antioxidant QSAR modeling as exemplified on polyphenols.
Lucić B; Amić D; Trinajstić N
Methods Mol Biol; 2008; 477():207-18. PubMed ID: 19082949
[TBL] [Abstract][Full Text] [Related]
28. Quantitative structure-activity relationships for a series of inhibitors of cruzain from Trypanosoma cruzi: molecular modeling, CoMFA and CoMSIA studies.
Trossini GH; Guido RV; Oliva G; Ferreira EI; Andricopulo AD
J Mol Graph Model; 2009 Aug; 28(1):3-11. PubMed ID: 19376735
[TBL] [Abstract][Full Text] [Related]
29. Novel inhibitors of human histone deacetylase (HDAC) identified by QSAR modeling of known inhibitors, virtual screening, and experimental validation.
Tang H; Wang XS; Huang XP; Roth BL; Butler KV; Kozikowski AP; Jung M; Tropsha A
J Chem Inf Model; 2009 Feb; 49(2):461-76. PubMed ID: 19182860
[TBL] [Abstract][Full Text] [Related]
30. SAR and QSAR modeling of endocrine disruptors.
Devillers J; Marchand-Geneste N; Carpy A; Porcher JM
SAR QSAR Environ Res; 2006 Aug; 17(4):393-412. PubMed ID: 16920661
[TBL] [Abstract][Full Text] [Related]
31. A structure-information approach to the prediction of biological activities and properties.
Hall LH
Chem Biodivers; 2004 Jan; 1(1):183-201. PubMed ID: 17191786
[TBL] [Abstract][Full Text] [Related]
32. Artificial neural networks-based approach to design ARIs using QSAR for diabetes mellitus.
Patra JC; Singh O
J Comput Chem; 2009 Nov; 30(15):2494-508. PubMed ID: 19373836
[TBL] [Abstract][Full Text] [Related]
33. Application of MOLMAP approach for QSAR modeling of various biological activities using substituent electronic descriptors.
Hemmateenejad B; Mehdipour AR; Miri R; Shamsipur M
J Comput Chem; 2009 Oct; 30(13):2001-9. PubMed ID: 19130500
[TBL] [Abstract][Full Text] [Related]
34. Multitemplate alignment method for the development of a reliable 3D-QSAR model for the analysis of MMP3 inhibitors.
Tuccinardi T; Ortore G; Santos MA; Marques SM; Nuti E; Rossello A; Martinelli A
J Chem Inf Model; 2009 Jul; 49(7):1715-24. PubMed ID: 19522467
[TBL] [Abstract][Full Text] [Related]
35. Short linear cationic antimicrobial peptides: screening, optimizing, and prediction.
Hilpert K; Fjell CD; Cherkasov A
Methods Mol Biol; 2008; 494():127-59. PubMed ID: 18726572
[TBL] [Abstract][Full Text] [Related]
36. Global, local and novel consensus quantitative structure-activity relationship studies of 4-(Phenylaminomethylene) isoquinoline-1, 3 (2H, 4H)-diones as potent inhibitors of the cyclin-dependent kinase 4.
Lei B; Xi L; Li J; Liu H; Yao X
Anal Chim Acta; 2009 Jun; 644(1-2):17-24. PubMed ID: 19463556
[TBL] [Abstract][Full Text] [Related]
37. Improvement of multivariate image analysis applied to quantitative structure-activity relationship (QSAR) analysis by using wavelet-principal component analysis ranking variable selection and least-squares support vector machine regression: QSAR study of checkpoint kinase WEE1 inhibitors.
Cormanich RA; Goodarzi M; Freitas MP
Chem Biol Drug Des; 2009 Feb; 73(2):244-52. PubMed ID: 19207427
[TBL] [Abstract][Full Text] [Related]
38. Comments on the definition of the Q2 parameter for QSAR validation.
Consonni V; Ballabio D; Todeschini R
J Chem Inf Model; 2009 Jul; 49(7):1669-78. PubMed ID: 19527034
[TBL] [Abstract][Full Text] [Related]
39. Alignment-free prediction of a drug-target complex network based on parameters of drug connectivity and protein sequence of receptors.
Viña D; Uriarte E; Orallo F; González-Díaz H
Mol Pharm; 2009; 6(3):825-35. PubMed ID: 19281186
[TBL] [Abstract][Full Text] [Related]
40. QSAR models for predicting enzymatic hydrolysis of new chemical entities in 'soft-drug' design.
Massarelli I; Macchia M; Minutolo F; Prota G; Bianucci AM
Bioorg Med Chem; 2009 May; 17(10):3543-56. PubMed ID: 19398207
[TBL] [Abstract][Full Text] [Related]
[Previous] [Next] [New Search]