ライフサイエンスコーパス: 3D-QSAR

1 3D-QSAR analyses of these benzil analogues for three dif

2 3D-QSAR analysis has shown that, within this series, spe

3 3D-QSAR models for human TRPV1 channel antagonists were

4 3D-QSAR models of cocaine binding were developed by comp

5 3D-QSAR partial least squares (PLS) cross-validation pro

6 Application of "topomer CoMFA" to 15 3D-QSAR analyses taken from the literature (847 structur

7 Using CATALYST, a 3D QSAR was generated that rationalizes the variation in

8 In addition, we devised a 3D QSAR using the atomic property field method.

9 A 3D-QSAR CoMFA study of piperidine-based analogues of coc

10 thermore, using previously published data, a 3D-QSAR model was developed for cocaine binding to the d

11 nts based descriptors were used to develop a 3D-QSAR model by aligning known active compounds onto id

12 We also developed a 3D-QSAR model for the binding of digoxin to the murine a

13 affinity for the CB1 receptor, we devised a 3D-QSAR model, which we then prospectively validated.

14 Furthermore, a 3D-QSAR model was derived using CoMFA for a training set

15 The subsequent data was used in a 3D-QSAR analysis using GRIND pharmacophore-based and phy

16 quadrupolar moments have been utilized in a 3D-QSAR analysis, and it is shown that descriptors invol

17 against this pharmacophore so as to obtain a 3D-QSAR model.

18 On the basis of a 3D-QSAR study, a new generation of tocainide analogues w

19 lysis (CoMFA) methods were used to produce a 3D-QSAR model that correlated the catalytic efficiency o

20 ensemble docking, hydropathic analysis, and 3D-QSAR provides an atomic-scale colchicine site model m

21 tional analyses, superimposition models, and 3D-QSAR models suggest that the N1 aromatic ring moiety

22 cle describes the development of field based 3D-QSAR model based on human breast cancer cell line MCF

23 that adapts ligand-based and receptor-based 3D-QSAR methods for use with cell-level activities.

24 y of dinitroaniline sulfonamides by CATALYST 3D-QSAR methodology, and this pharmacophore was used to

25 with multiple iterations, yielding Catalyst 3D-QSAR models being able to qualitatively rank-order an

26 lts, alignments I and II produced comparable 3D-QSAR models with alignment II being slightly better t

27 CoMFA and CoMSIA 3D QSAR models were also derived using a molecular align

28 The CoMSIA 3D QSAR models performed better than the CoMFA models.

29 e subjected to CoMFA, CoMFA+HINT, and CoMSIA 3D-QSAR analyses.

30 ative molecular similarity analysis (CoMSIA) 3D-QSAR studies on 50 benzylidene malonitrile derivative

31 Various "enhanced" 3D-QSAR models were constructed in which different combi

32 for the same data set from other established 3D QSAR methods.

33 Furthermore, excellent 3D QSAR correlates were obtained for two human CEs, hCE1

34 akes them unsuitable candidates for existing 3D-QSAR methods and has led us to develop an alternative

35 re also found in the set of significant FEFF 3D-QSAR models.

36 The FEFF 3D-QSAR models can be used to estimate the binding free

37 n data fitting, was used to develop the FEFF 3D-QSAR models for the binding process and to scale the

38 Free energy force field (FEFF) 3D-QSAR analysis was used to construct ligand-receptor b

39 Finally, 3D QSAR studies confirmed our SAR findings that three bu

40 All five 3D-QSAR models yielded cross-validated q(2) values great

41 For 3D QSAR studies, based on the multiple binding modes obt

42 structure provided a reliable alignment for 3D-QSAR models.

43 receptors represents an attainable goal for 3D-QSAR.

44 The obtained 3D QSAR model was subsequently compared with the X-ray s

45 independent variables in the development of 3D-QSAR models by correlating these energy terms with ex

46 We report the results of 3D-QSAR/CoMFA investigations of the activity of bisphosp

47 We compared the formation of 3D-QSARs using standard CoMFA with the use of ILP on the

48 Predictive 3D QSAR models were established using SYBYL multifit mol

49 l docking studies and generated a predictive 3D-QSAR model for SARS-CoV PLpro inhibitors.

50 These statistically predictive 3D-QSAR models indicate that both binding sites are abou

51 nto the X-ray structure of 13d then provided 3D-QSAR models for NHE3 inhibition capturing guidelines

52 The study represents the first quantitative 3D-QSAR model for NMDA receptor blockade, and it compris

53 uantitative structure-activity relationship (3D QSAR) models for the inhibitory activity against Pneu

54 uantitative structure-activity relationship (3D QSAR) studies and docking simulations were conducted

55 uantitative structure-affinity relationship (3D QSAR) studies using comparative molecular field analy

56 uantitative structure-activity relationship (3D-QSAR) and pharmacophore modeling investigation of the

57 uantitative structure-activity relationship (3D-QSAR) applying comparative molecular field analysis (

58 uantitative structure-activity relationship (3D-QSAR) for nonsteroidal estrogen receptor (ER) ligands

59 uantitative structure-activity relationship (3D-QSAR) methodology.

60 uantitative structure-activity relationship (3D-QSAR) model for the inhibition of Na(+),K(+)-ATPase u

61 uantitative structure-activity relationship (3D-QSAR) models constructed using comparative molecular

62 uantitative structure-activity relationship (3D-QSAR) models for ligand binding to 1B3 and to three a

63 uantitative structure-activity relationship (3D-QSAR) models have been developed using comparative mo

64 uantitative structure-activity relationship (3D-QSAR) models have been obtained using comparative mol

65 uantitative structure-activity relationship (3D-QSAR) models on the basis of comparative molecular fi

66 uantitative structure activity relationship (3D-QSAR) models that qualitatively rank and predict IC(5

67 uantitative structure-activity relationship (3D-QSAR) models using comparative molecular field analys

68 uantitative structure-activity relationship (3D-QSAR) models were built using the docked poses of 29

69 uantitative structure-activity relationship (3D-QSAR) models were constructed using comparative molec

70 uantitative structure-activity relationship (3D-QSAR) models were constructed using comparative molec

71 uantitative structure-activity relationship (3D-QSAR) models were generated using in vitro data assoc

72 uantitative structure-activity relationship (3D-QSAR) software program, Catalyst.

73 uantitative structure-activity relationship (3D-QSAR) study was performed on a series of mazindol ana

74 uantitative structure-activity relationship (3D-QSAR) study, utilizing comparative molecular field an

75 uantitative structure-activity relationship (3D-QSAR) was evaluated against a test set of SP antagoni

76 antitative structure-activity relationships (3D-QSAR), is herein extended to consider both affinity a

77 pproximately equal to 0.80) of the resultant 3D-QSAR model.

78 The resulting 3D-QSAR has the most predictive potential of the models

79 The resulting 3D-QSAR indicates a strong correlation between the inhib

80 gnificant FEFF energy terms in the resulting 3D-QSAR models include the intramolecular vacuum energy

81 ive structure-activity relationship studies (3D-QSAR) presents a unique opportunity for accuracy and

82 Such systematic 3D-QSAR/CoMFA analyses of 29 molecules and their recepto

83 The 3D-QSAR models developed, relating the hepatoprotection

84 The 3D-QSAR models obtained from CoMFA using standard partia

85 ion nicely correlate with the results of the 3D-QSAR analysis.

86 on between two molecules at any stage of the 3D-QSAR calculation.

87 milarly minimized and aligned to produce the 3D-QSAR models.

88 yclic aromatic hydrocarbons (PAHs) using the 3D-QSAR method known as comparative molecular field anal

89 from eight scaffolds were evaluated with the 3D-QSAR models, which correctly ranked their activity tr

90 These 3D-QSAR models and their respective contour plots should

91 These 3D-QSAR models will be useful for future prediction of l

92 d experimental potency values points to this 3D QSAR model as the first example of quantitative struc

93 ecular field analysis (CoMFA) to develop two 3D-QSAR (quantitative structure-activity relationship) m

94 We then used 3D QSAR (comparative molecular field and comparative mol

95 tes as gammadelta T cell activators by using 3D QSAR techniques can be expected to help facilitate th

96 redict the activity of bisphosphonates using 3D-QSAR/CoMFA methods, although bone resorption studies

97 redicted within about a factor of 3 by using 3D-QSAR techniques.