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1 microbial identification and the analysis of quantitative structure-activity relationships.
3 , we previously built Catalyst 3-dimensional quantitative structure activity relationship (3D-QSAR) m
4 ic pathogens, we developed three-dimensional quantitative structure-activity relationship (3D QSAR) m
5 indices analysis (CoMSIA) three-dimensional quantitative structure-activity relationship (3D QSAR) s
6 asis for the third step, a three-dimensional quantitative structure-activity relationship (3D-QSAR) a
7 We report the results of a three-dimensional quantitative structure-activity relationship (3D-QSAR) a
8 MFA/q2-GRS) method, has been used to build a quantitative structure-activity relationship (3D-QSAR) f
9 s were employed to develop three-dimensional quantitative structure-activity relationship (3D-QSAR) m
11 additional descriptors to three-dimensional quantitative structure-activity relationship (3D-QSAR) m
13 l steroids and developed a three-dimensional quantitative structure-activity relationship (3D-QSAR) m
17 were proposed and several three-dimensional quantitative structure-activity relationship (3D-QSAR) m
20 man sequence mAb 2E2 using three-dimensional quantitative structure-activity relationship (3D-QSAR) m
22 was accomplished using the three-dimensional quantitative structure-activity relationship (3D-QSAR) s
24 e report the synthesis and three-dimensional quantitative structure-activity relationship (3D-QSAR) s
25 en used as the basis for a three-dimensional quantitative structure-activity relationship (3D-QSAR) s
26 e ability of the resulting three-dimensional quantitative structure-activity relationship (3D-QSAR) w
27 e to derive receptor-based three-dimensional quantitative structure-activity relationships (3D-QSAR),
28 multivariate discriminant, fragment, and 3D-quantitative structure-activity relationship analyses, w
29 und, 2-octynoic acid, was unique in both its quantitative structure-activity relationship analysis an
33 Secondly, new and extended methods of QSAR (quantitative structure-activity relationship) analysis h
34 sment Tool for Evaluating Risk (ASTER) QSAR (quantitative structure activity relationship) applicatio
35 animal data and outperformed 12 conventional quantitative structure-activity relationship approaches.
37 ploy HFE cosolvents, we have established the quantitative structure-activity relationship between the
38 ented herein builds on the important work in quantitative structure-activity relationships by linking
40 ng concern in central nervous system-related quantitative structure-activity relationship (CNS-QSAR)
41 ased on these data, two highly predictive 3D quantitative structure-activity relationship (comparativ
42 Using three-dimensional quantitative structure-activity relationship/comparative
43 To address this hypothesis, based upon our quantitative structure-activity relationship data, a tot
45 nfortunately, precludes the development of a quantitative structure-activity relationship for permang
46 nt-Frizzled CRD interactions and developed a quantitative structure-activity relationship for predict
47 alysis (CoMFA) to obtain a three-dimensional quantitative structure-activity relationship for pyridin
50 r field analysis (CoMFA) was used to develop quantitative structure-activity relationships for physos
51 f the esters with the needed biostability, a quantitative structure-activity relationship has been de
53 o this 3D QSAR model as the first example of quantitative structure-activity relationships in the fie
55 lied a variable selection k nearest neighbor quantitative structure-activity relationship (kNN QSAR)
56 computed here and available, nonexperimental quantitative structure-activity relationship literature
57 igand-based computational approaches (binary quantitative structure-activity relationship), medicinal
58 iation of well-established three-dimensional quantitative structure--activity relationship methodolog
59 amides (MBSAs), we applied three-dimensional quantitative structure-activity relationship methods, co
61 efficient fitness function based on a linear quantitative structure-activity relationship model for c
62 weeteners with known sweetness values, a new quantitative structure-activity relationship model for s
64 ts of this study have been used to develop a quantitative structure-activity relationship model with
65 Using a homology and a three-dimensional quantitative structure-activity relationship model, a bi
66 duration in vivo and in vitro bioassays, and quantitative structure activity relationship modeling.
67 n integrated high-throughput experimentation/quantitative structure-activity relationship modeling ap
69 ng and chemical elaboration combined with 3D-quantitative structure-activity relationship modeling yi
70 ses structure-based docking and ligand-based quantitative structure-activity relationship modeling.
73 kflow for creating and validating predictive Quantitative Structure-Activity Relationship models and
74 rmations were used to build CoMFA and CoMSIA quantitative structure-activity relationship models.
75 eld analysis (CoMFA) to develop two 3D-QSAR (quantitative structure-activity relationship) models (Co
76 g lead optimization, while 3D-shape or QSAR (quantitative structure-activity relationship) models pro
77 mary descriptors in development of the QSAR (quantitative structure-activity relationships) of flavon
78 and experiments are underway to establish a quantitative structure-activity relationship on a limite
79 in drug screening, drug toxicity prediction, quantitative structure-activity relationship prediction,
82 em mass spectrometry (LC-MS/MS), qualitative/quantitative structure activity relationship (QSAR) and
85 zylamine analogues show Y444F MAO A exhibits quantitative structure activity relationships (QSAR) pro
86 and compare the steric parameters common in quantitative structure activity relationships (QSAR), a
93 oactivity profile of compounds in silico and quantitative structure-activity relationship (QSAR) anal
94 Amino acid descriptors are often used in quantitative structure-activity relationship (QSAR) anal
99 lecular field topology analysis (MFTA), a 2D quantitative structure-activity relationship (QSAR) appr
100 ites for THDCs targeting TTR, we developed a quantitative structure-activity relationship (QSAR) clas
103 tified by GC x GC-TOFMS were confirmed using quantitative structure-activity relationship (QSAR) esti
106 e ends have made use of two-dimensional (2D) quantitative structure-activity relationship (QSAR) meth
110 port the development of rigorously validated quantitative structure-activity relationship (QSAR) mode
111 was used to construct four-dimensional (4D) quantitative structure-activity relationship (QSAR) mode
113 employed to construct three-dimensional (3D)-quantitative structure-activity relationship (QSAR) mode
114 applied in the construction of parsimonious quantitative structure-activity relationship (QSAR) mode
116 process that utilizes a battery of in-house quantitative structure-activity relationship (QSAR) mode
118 etic (PBPK) model by integrating an AI-based quantitative structure-activity relationship (QSAR) mode
119 is study, we selected 603 compounds by using quantitative structure-activity relationship (QSAR) mode
120 ally deep learning, shows great potential in quantitative structure-activity relationship (QSAR) mode
126 discriminant analysis (PLS-DA) combined with quantitative structure-activity relationship (QSAR) mode
127 eir assigned relative biodegradabilities and quantitative structure-activity relationship (QSAR) mode
128 oyed these data to build and validate binary quantitative structure-activity relationship (QSAR) mode
130 be useful to employ toxicity estimates from quantitative structure-activity relationship (QSAR) mode
132 (sigma*) constants from previously developed quantitative structure-activity relationship (QSAR) mode
133 rom soy proteins using LC-MS/MS coupled with quantitative structure-activity relationship (QSAR) mode
136 , we have developed and rigorously validated quantitative structure-activity relationship (QSAR) mode
139 parameters were then used to develop several quantitative structure-activity relationship (QSAR) mode
140 utilized these data to develop the following quantitative structure-activity relationship (QSAR) mode
141 ery strategy that employs variable selection quantitative structure-activity relationship (QSAR) mode
142 ere used as a test set for validation of the quantitative structure-activity relationship (QSAR) mode
144 terized the electrophysiology, kinetics, and quantitative structure-activity relationship (QSAR) of t
145 ro ToxCast binding assays to assess OASIS, a quantitative structure-activity relationship (QSAR) plat
146 xperimental results were further compared to quantitative structure-activity relationship (QSAR) pred
147 cy of incorrect categorization compared to a quantitative structure-activity relationship (QSAR) regr
149 ceptor has been examined through Hansch-type quantitative structure-activity relationship (QSAR) stud
151 ficial neural network has been developed for quantitative structure-activity relationship (QSAR) stud
152 d bifunctional analogues are reported, and a quantitative structure-activity relationship (QSAR) stud
153 ed molecular conformers as templates, the 3D quantitative structure-activity relationship (QSAR) stud
155 l and synthetic phosphoantigens by using the quantitative structure-activity relationship (QSAR) tech
157 This correlation was used to calibrate a new quantitative structure-activity relationship (QSAR) usin
159 substrate design, DT-diaphorase-cytotoxicity quantitative structure-activity relationship (QSAR), and
163 work (GNN), has been developed for obtaining quantitative structure-activity relationships (QSAR) for
164 ty of genetic neural network (GNN) to obtain quantitative structure-activity relationships (QSAR) fro
166 xpensive, we developed binary and continuous Quantitative Structure-Activity Relationships (QSAR) mod
167 This field of research, broadly known as quantitative structure-activity relationships (QSAR) mod
168 ave been employed as training sets to create quantitative structure-activity relationships (QSAR) whi
169 nstants and the substituent parameters using quantitative structure-activity relationships (QSAR).
173 , scaled by potency differences predicted by quantitative structure-activity relationships (QSARs) fo
174 d is presented for developing and evaluating Quantitative Structure-Activity Relationships (QSARs) fo
175 teric similarity matrices (SM/GNN) to obtain quantitative structure-activity relationships (QSARs) is
176 was applied to develop and evaluate various quantitative structure-activity relationships (QSARs) to
178 All of the correlations gave satisfactory quantitative structure-activity relationships (QSARs), b
179 new biomaterials is hindered by the lack of quantitative structure-activity relationships (QSARs).
180 all molecules, including a lack of validated quantitative structure-activity relationships (QSARs).
181 ze the results into predictive models (e.g., quantitative structure-activity relationships, QSARs) fo
183 ional approaches for drug discovery, such as quantitative structure-activity relationship, rely on st
184 lecular dynamics simulations combined with a quantitative structure activity relationship revealed th
185 approaches focused on the identification of quantitative structure-activity relationship (SAR) for e
187 y for lead discovery, lead optimization, and quantitative structure activity relationship studies has
188 the dependent variable in three-dimensional quantitative structure-activity relationship studies (3D
191 The synthesis, pharmacological testing, and quantitative structure-activity relationship studies of
195 rest since they represent the first detailed quantitative structure-activity relationship study of th
197 structural basis, we used three-dimensional quantitative structure-activity relationship techniques:
198 s, the program HINT was used to develop a 3D quantitative structure activity relationship that predic
202 Rate constants are reasonably described by a quantitative structure-activity relationship with phenol