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1 , we previously built Catalyst 3-dimensional quantitative structure activity relationship (3D-QSAR) m
2 ic pathogens, we developed three-dimensional quantitative structure-activity relationship (3D QSAR) m
3 indices analysis (CoMSIA) three-dimensional quantitative structure-activity relationship (3D QSAR) s
4 asis for the third step, a three-dimensional quantitative structure-activity relationship (3D-QSAR) a
5 We report the results of a three-dimensional quantitative structure-activity relationship (3D-QSAR) a
6 MFA/q2-GRS) method, has been used to build a quantitative structure-activity relationship (3D-QSAR) f
8 l steroids and developed a three-dimensional quantitative structure-activity relationship (3D-QSAR) m
12 were proposed and several three-dimensional quantitative structure-activity relationship (3D-QSAR) m
15 man sequence mAb 2E2 using three-dimensional quantitative structure-activity relationship (3D-QSAR) m
16 s were employed to develop three-dimensional quantitative structure-activity relationship (3D-QSAR) m
18 additional descriptors to three-dimensional quantitative structure-activity relationship (3D-QSAR) m
19 was accomplished using the three-dimensional quantitative structure-activity relationship (3D-QSAR) s
20 en used as the basis for a three-dimensional quantitative structure-activity relationship (3D-QSAR) s
22 e ability of the resulting three-dimensional quantitative structure-activity relationship (3D-QSAR) w
23 e to derive receptor-based three-dimensional quantitative structure-activity relationships (3D-QSAR),
24 multivariate discriminant, fragment, and 3D-quantitative structure-activity relationship analyses, w
25 und, 2-octynoic acid, was unique in both its quantitative structure-activity relationship analysis an
28 Secondly, new and extended methods of QSAR (quantitative structure-activity relationship) analysis h
29 sment Tool for Evaluating Risk (ASTER) QSAR (quantitative structure activity relationship) applicatio
31 ented herein builds on the important work in quantitative structure-activity relationships by linking
33 ased on these data, two highly predictive 3D quantitative structure-activity relationship (comparativ
34 Using three-dimensional quantitative structure-activity relationship/comparative
35 To address this hypothesis, based upon our quantitative structure-activity relationship data, a tot
37 nt-Frizzled CRD interactions and developed a quantitative structure-activity relationship for predict
38 alysis (CoMFA) to obtain a three-dimensional quantitative structure-activity relationship for pyridin
41 r field analysis (CoMFA) was used to develop quantitative structure-activity relationships for physos
42 f the esters with the needed biostability, a quantitative structure-activity relationship has been de
44 o this 3D QSAR model as the first example of quantitative structure-activity relationships in the fie
45 lied a variable selection k nearest neighbor quantitative structure-activity relationship (kNN QSAR)
46 igand-based computational approaches (binary quantitative structure-activity relationship), medicinal
47 iation of well-established three-dimensional quantitative structure--activity relationship methodolog
48 amides (MBSAs), we applied three-dimensional quantitative structure-activity relationship methods, co
49 efficient fitness function based on a linear quantitative structure-activity relationship model for c
50 weeteners with known sweetness values, a new quantitative structure-activity relationship model for s
51 ts of this study have been used to develop a quantitative structure-activity relationship model with
52 Using a homology and a three-dimensional quantitative structure-activity relationship model, a bi
53 duration in vivo and in vitro bioassays, and quantitative structure activity relationship modeling.
55 ng and chemical elaboration combined with 3D-quantitative structure-activity relationship modeling yi
57 kflow for creating and validating predictive Quantitative Structure-Activity Relationship models and
58 rmations were used to build CoMFA and CoMSIA quantitative structure-activity relationship models.
59 eld analysis (CoMFA) to develop two 3D-QSAR (quantitative structure-activity relationship) models (Co
60 mary descriptors in development of the QSAR (quantitative structure-activity relationships) of flavon
61 and experiments are underway to establish a quantitative structure-activity relationship on a limite
63 em mass spectrometry (LC-MS/MS), qualitative/quantitative structure activity relationship (QSAR) and
65 zylamine analogues show Y444F MAO A exhibits quantitative structure activity relationships (QSAR) pro
66 and compare the steric parameters common in quantitative structure activity relationships (QSAR), a
71 oactivity profile of compounds in silico and quantitative structure-activity relationship (QSAR) anal
72 Amino acid descriptors are often used in quantitative structure-activity relationship (QSAR) anal
77 lecular field topology analysis (MFTA), a 2D quantitative structure-activity relationship (QSAR) appr
78 ites for THDCs targeting TTR, we developed a quantitative structure-activity relationship (QSAR) clas
81 tified by GC x GC-TOFMS were confirmed using quantitative structure-activity relationship (QSAR) esti
84 e ends have made use of two-dimensional (2D) quantitative structure-activity relationship (QSAR) meth
87 process that utilizes a battery of in-house quantitative structure-activity relationship (QSAR) mode
88 applied in the construction of parsimonious quantitative structure-activity relationship (QSAR) mode
90 be useful to employ toxicity estimates from quantitative structure-activity relationship (QSAR) mode
91 (sigma*) constants from previously developed quantitative structure-activity relationship (QSAR) mode
92 rom soy proteins using LC-MS/MS coupled with quantitative structure-activity relationship (QSAR) mode
95 , we have developed and rigorously validated quantitative structure-activity relationship (QSAR) mode
98 eir assigned relative biodegradabilities and quantitative structure-activity relationship (QSAR) mode
99 parameters were then used to develop several quantitative structure-activity relationship (QSAR) mode
100 utilized these data to develop the following quantitative structure-activity relationship (QSAR) mode
101 ery strategy that employs variable selection quantitative structure-activity relationship (QSAR) mode
102 ere used as a test set for validation of the quantitative structure-activity relationship (QSAR) mode
104 port the development of rigorously validated quantitative structure-activity relationship (QSAR) mode
105 was used to construct four-dimensional (4D) quantitative structure-activity relationship (QSAR) mode
106 employed to construct three-dimensional (3D)-quantitative structure-activity relationship (QSAR) mode
108 terized the electrophysiology, kinetics, and quantitative structure-activity relationship (QSAR) of t
109 ro ToxCast binding assays to assess OASIS, a quantitative structure-activity relationship (QSAR) plat
111 ceptor has been examined through Hansch-type quantitative structure-activity relationship (QSAR) stud
113 ficial neural network has been developed for quantitative structure-activity relationship (QSAR) stud
114 d bifunctional analogues are reported, and a quantitative structure-activity relationship (QSAR) stud
115 ed molecular conformers as templates, the 3D quantitative structure-activity relationship (QSAR) stud
117 l and synthetic phosphoantigens by using the quantitative structure-activity relationship (QSAR) tech
119 This correlation was used to calibrate a new quantitative structure-activity relationship (QSAR) usin
121 substrate design, DT-diaphorase-cytotoxicity quantitative structure-activity relationship (QSAR), and
124 work (GNN), has been developed for obtaining quantitative structure-activity relationships (QSAR) for
125 ty of genetic neural network (GNN) to obtain quantitative structure-activity relationships (QSAR) fro
127 ave been employed as training sets to create quantitative structure-activity relationships (QSAR) whi
128 nstants and the substituent parameters using quantitative structure-activity relationships (QSAR).
129 d is presented for developing and evaluating Quantitative Structure-Activity Relationships (QSARs) fo
130 teric similarity matrices (SM/GNN) to obtain quantitative structure-activity relationships (QSARs) is
131 was applied to develop and evaluate various quantitative structure-activity relationships (QSARs) to
132 All of the correlations gave satisfactory quantitative structure-activity relationships (QSARs), b
133 ze the results into predictive models (e.g., quantitative structure-activity relationships, QSARs) fo
135 approaches focused on the identification of quantitative structure-activity relationship (SAR) for e
137 y for lead discovery, lead optimization, and quantitative structure activity relationship studies has
138 the dependent variable in three-dimensional quantitative structure-activity relationship studies (3D
141 The synthesis, pharmacological testing, and quantitative structure-activity relationship studies of
145 rest since they represent the first detailed quantitative structure-activity relationship study of th
147 structural basis, we used three-dimensional quantitative structure-activity relationship techniques:
148 s, the program HINT was used to develop a 3D quantitative structure activity relationship that predic
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