ライフサイエンスコーパス: QSAR

1 QSAR analyses demonstrated excellent observed versus pre

2 QSAR analysis indicated neither the first electron trans

3 QSAR has been used to elucidate the origin of the hydrop

4 QSAR modeling can be used to aid testing prioritization

5 QSAR models based on IL-1beta were able to predict the i

6 QSAR models developed in these studies shall aid in futu

7 QSAR models were developed for inflammatory potential, w

8 QSAR models were generated using multiple topological de

9 QSAR models with high internal accuracy were generated,

10 QSAR studies were carried out using the mean 50% growth

11 lecular field topology analysis (MFTA), a 2D QSAR approach based on local molecular properties (atomi

12 Herein we describe how the use of SBDD, 2D QSAR models, and matched molecular pair data in compound

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

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

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

16 CoMFA and CoMSIA 3D QSAR models were also derived using a molecular alignmen

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

18 Finally, 3D QSAR studies confirmed our SAR findings that three bulky

19 For 3D QSAR studies, based on the multiple binding modes obtain

20 The obtained 3D QSAR model was subsequently compared with the X-ray stru

21 Predictive 3D QSAR models were established using SYBYL multifit molecu

22 titative structure-activity relationship (3D QSAR) models for the inhibitory activity against Pneumoc

23 titative structure-activity relationship (3D QSAR) studies and docking simulations were conducted on

24 titative structure-affinity relationship (3D QSAR) studies using comparative molecular field analysis

25 xperimental potency values points to this 3D QSAR model as the first example of quantitative structur

26 We then used 3D QSAR (comparative molecular field and comparative molecu

27 as gammadelta T cell activators by using 3D QSAR techniques can be expected to help facilitate the d

28 3D-QSAR analyses of these benzil analogues for three differ

29 3D-QSAR analysis has shown that, within this series, specif

30 3D-QSAR models for human TRPV1 channel antagonists were dev

31 3D-QSAR models of cocaine binding were developed by compara

32 Application of "topomer CoMFA" to 15 3D-QSAR analyses taken from the literature (847 structures)

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

34 A 3D-QSAR CoMFA study of piperidine-based analogues of cocain

35 based descriptors were used to develop a 3D-QSAR model by aligning known active compounds onto ident

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

37 is (CoMFA) methods were used to produce a 3D-QSAR model that correlated the catalytic efficiency of r

38 rmore, using previously published data, a 3D-QSAR model was developed for cocaine binding to the dopa

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

40 inst this pharmacophore so as to obtain a 3D-QSAR model.

41 On the basis of a 3D-QSAR study, a new generation of tocainide analogues were

42 nal analyses, superimposition models, and 3D-QSAR models suggest that the N1 aromatic ring moiety of

43 semble docking, hydropathic analysis, and 3D-QSAR provides an atomic-scale colchicine site model more

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

45 describes the development of field based 3D-QSAR model based on human breast cancer cell line MCF7 i

46 f dinitroaniline sulfonamides by CATALYST 3D-QSAR methodology, and this pharmacophore was used to sea

47 th multiple iterations, yielding Catalyst 3D-QSAR models being able to qualitatively rank-order and p

48 , alignments I and II produced comparable 3D-QSAR models with alignment II being slightly better than

49 ubjected to CoMFA, CoMFA+HINT, and CoMSIA 3D-QSAR analyses.

50 ve molecular similarity analysis (CoMSIA) 3D-QSAR studies on 50 benzylidene malonitrile derivatives.

51 Various "enhanced" 3D-QSAR models were constructed in which different combinat

52 s them unsuitable candidates for existing 3D-QSAR methods and has led us to develop an alternative ap

53 All five 3D-QSAR models yielded cross-validated q(2) values greater

54 ructure provided a reliable alignment for 3D-QSAR models.

55 We report the results of 3D-QSAR/CoMFA investigations of the activity of bisphosphon

56 ocking studies and generated a predictive 3D-QSAR model for SARS-CoV PLpro inhibitors.

57 These statistically predictive 3D-QSAR models indicate that both binding sites are about 2

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

59 titative structure-activity relationship (3D-QSAR) and pharmacophore modeling investigation of the in

60 titative structure-activity relationship (3D-QSAR) methodology.

61 titative structure-activity relationship (3D-QSAR) model for the inhibition of Na(+),K(+)-ATPase usin

62 titative structure-activity relationship (3D-QSAR) models constructed using comparative molecular fie

63 titative structure-activity relationship (3D-QSAR) models for ligand binding to 1B3 and to three addi

64 titative structure-activity relationship (3D-QSAR) models on the basis of comparative molecular field

65 titative structure activity relationship (3D-QSAR) models that qualitatively rank and predict IC(50)

66 titative structure-activity relationship (3D-QSAR) models were built using the docked poses of 29 (th

67 titative structure-activity relationship (3D-QSAR) models were constructed using comparative molecula

68 titative structure-activity relationship (3D-QSAR) models were constructed using comparative molecula

69 titative structure-activity relationship (3D-QSAR) models were generated using in vitro data associat

70 titative structure-activity relationship (3D-QSAR) studies of a new family of bicyclic alpha-iminopho

71 titative structure-activity relationship (3D-QSAR) study was performed on a series of mazindol analog

72 titative structure-activity relationship (3D-QSAR) study, utilizing comparative molecular field analy

73 itative structure-activity relationships (3D-QSAR), is herein extended to consider both affinity and

74 oximately equal to 0.80) of the resultant 3D-QSAR model.

75 Such systematic 3D-QSAR/CoMFA analyses of 29 molecules and their receptor a

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

77 The 3D-QSAR models developed, relating the hepatoprotection act

78 The 3D-QSAR models obtained from CoMFA using standard partial l

79 m eight scaffolds were evaluated with the 3D-QSAR models, which correctly ranked their activity trend

80 arly minimized and aligned to produce the 3D-QSAR models.

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

82 These 3D-QSAR models will be useful for future prediction of like

83 icted within about a factor of 3 by using 3D-QSAR techniques.

84 ict the activity of bisphosphonates using 3D-QSAR/CoMFA methods, although bone resorption studies sho

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

86 nds was used to evaluate the tadpole LORR 4D-QSAR model.

87 ative structure-activity relationship (RD-4D-QSAR) analysis is used to map the ligand-receptor bindin

88 The three resulting 4D-QSAR models are almost identical in form, and all sugges

89 he third postulated binding site from the 4D-QSAR models.

90 The 5D-QSAR model was developed stepwise.

91 A QSAR model highlighted the importance of lipophilicity a

92 A QSAR using Comparative Molecular Field Analysis (CoMFA)

93 substituted derivatives were prepared and a QSAR model was generated, which allowed accurate predict

94 tric definition of shape described here as a QSAR variable, for instance, in the prediction or classi

95 and standard regression techniques to give a QSAR method that has the strength of ILP at describing s

96 ers developed by Verloop and co-workers in a QSAR context.

97 Activity data have been incorporated into a QSAR with predictive power, and the X-ray crystal struct

98 This paper describes the development of a QSAR model for the rational control of functional durati

99 be an approach for applying the results of a QSAR model from the TOPKAT program suite, which provides

100 In parallel, a QSAR model identified distinct molecular properties of M

101 complex, we sought to predict SAR through a QSAR model developed in house.

102 mmend a tiered screening approach wherein a) QSAR is used to identify compounds in-domain of the ER o

103 Additional QSAR models were developed to predict the biodistributio

104 Additionally, QSAR-like models based on the molecular mechanics (MM) w

105 -N-methyl prop-2-yn-1-amine (II, ASS234) and QSAR predictions, in this work we have designed, synthes

106 On the basis of molecular modeling and QSAR analysis of the known human progesterone receptor (

107 Employing structure- and QSAR-based drug design strategies, we rationally designe

108 Herein, we report on theoretical and QSAR investigations of a series of 53 novel bis-, tris-,

109 ences between the cytotoxicity and apoptosis QSAR for electron-releasing phenols suggest that cytotox

110 inding data set to show that OASIS ER and AR QSAR models had high sensitivity and specificity when co

111 ompounds within the domains of the ER and AR QSAR models that bound with AC50 < 1 muM, the QSAR model

112 ASsessment Tool for Evaluating Risk (ASTER) QSAR (quantitative structure activity relationship) appl

113 red with standard molecular descriptor-based QSAR; the latter was not found to provide superior predi

114 Fragment-based QSAR analyses relating the polar termini with cancer cel

115 The best QSAR model was selected on the basis of the predictive a

116 Initially, binary QSAR models for inhibition of SmTGR were developed and v

117 e resulting alignment was also used to build QSAR based on CoMFA, CoMSIA, and molecular descriptors.

118 ailable experimental data were used to build QSAR models and ligand- and structure-based pharmacophor

119 is process typically involve either building QSAR models or performing free energy calculations of th

120 eared slightly different when represented by QSAR colored surfaces, the combined model seems to recon

121 The calibrated cell-QSAR model is significantly more predictive than other m

122 tative structure-activity relationship (cell-QSAR) concept that adapts ligand-based and receptor-base

123 The resulting cell-QSAR model was applied to the Selwood data on filaricida

124 bination of virtual combinatorial chemistry, QSAR modeling, and molecular docking studies, a series o

125 chromatographic study were used to construct QSAR models of the NCI-nAChR binding with both electroni

126 This was used to create QSAR models to predict two types of end points: acute LC

127 We previously developed a preliminary 3-D QSAR model for the binding of 14 hydantoins to the neuro

128 obtained antitumor measurements, 3D-derived QSAR analysis was performed for the set of compounds.

129 Highly predictive 3D-derived QSAR models were obtained, and molecular properties that

130 (MeONPs) as a model ENM, we aimed to develop QSAR models for prediction of the inflammatory potential

131 lecules (benzene sulfonamides) and developed QSAR models for inhibition of this protein.

132 e data demonstrate that rigorously developed QSAR models can serve as reliable virtual screening tool

133 All three-dimensional QSAR approaches have a requirement for some hypothesis o

134 We have developed a three-dimensional QSAR pharmacophore model for inhibition of a Plasmodium

135 ds in-domain and predicted to bind by the ER QSAR model that were positive in ToxCast ER binding at A

136 We established QSAR models for prediction of MeONP-induced inflammatory

137 We further explored QSAR of bacterial reduction of different nitro compounds

138 A simple four-feature QSAR model was derived to rationalize MIC results in thi

139 ound to increase the complexity of the final QSAR equations and gave possible insight into the bindin

140 in vivo biotransformation rate database for QSAR development and in vitro to in vivo biotransformati

141 t this discussion, we provide guidelines for QSAR development, validation, and application, which are

142 on human serum protein binding reported for QSAR modeling.

143 escriptor importances from the random forest QSAR method show that other factors than the immediate c

144 d the results of the site-dependent fragment QSAR analysis led to the discovery and synthesis of a no

145 s obtained when racemates were excluded from QSAR analysis.

146 The generated QSAR (r(2) = 0.88), and LSER (r(2) = 0.83) equations wer

147 The HLT and HLB QSARs show similar statistical performance; that is, r(2

148 olecular field analysis (CoMFA) and hologram QSAR (HQSAR), beginning with a series of 211 artemisinin

149 summarizes recent technological advances in QSAR modeling but it also highlights the applicability o

150 thods, and validation practices developed in QSAR to a wide range of research areas outside of tradit

151 A key problem in QSAR is the selection of appropriate descriptors to form

152 an alternative to sigma and sigma*-values in QSAR applications and can also be utilized to estimate u

153 In this work, we integrated QSAR-based virtual screening (VS) of Schistosoma mansoni

154 n be learned from our history of integrating QSAR and structure-based methods into drug discovery.

155 kNN QSAR models were compared with those obtained with the c

156 itative structure-activity relationship (kNN QSAR) method to develop predictive QSAR models for 157 e

157 ive molecular field analysis method; the kNN QSAR approach afforded models with higher values of both

158 Molecular modeling (QSAR analysis) was conducted in an attempt to rationaliz

159 while five properties are specific to the MR QSAR model.

160 compared to another 2D fragment-based HL(N) QSAR developed with expert judgment, and the predictive

161 Results from the new HL(N) QSARs are compared to another 2D fragment-based HL(N) QS

162 tific standards to manuscripts reporting new QSAR studies, as well as encourage the use of high quali

163 of each method are investigated and the new QSAR is found to make comparable predictions with signif

164 Multilinear and nonlinear QSAR models were built for the skin permeation rate (Log

165 ion, the method is applied to generate novel QSARs for fish primary biotransformation half-lives (HL(

166 nd HLB from chemical structure and two novel QSARs are detailed.

167 Until now, QSAR/QSTR models have predicted ecotoxicity or cytotoxic

168 Evaluation of OASIS QSAR models using ToxCast in vitro estrogen and androgen

169 dance, and applicability domain of two OASIS QSAR models.

170 To provide an objective QSAR methodology that might accelerate lead optimization

171 ) several novel and emerging applications of QSAR modeling.

172 We propose that a combination of QSAR prediction and the zebrafish model organism is effi

173 highlighting the generalizable components of QSAR modeling will serve to address this challenge.

174 discuss (i) the development and evolution of QSAR; (ii) the current trends, unsolved problems, and pr

175 The relationship of QSAR predictions of binding to transactivation- and path

176 1 muM), we also examined the relationship of QSAR predictions of ER or AR binding to the results from

177 The robustness of QSAR models was characterized by the values of the inter

178 toward collaborative development and use of QSAR models.

179 porting lead optimization, while 3D-shape or QSAR (quantitative structure-activity relationship) mode

180 Unlike other QSAR methods, which use attributes to describe chemical

181 r their affinities to be consistent with our QSAR analysis of the entire set of 17 molecules.

182 ith a conformationally sampled pharmacophore/QSAR modeling approach (CSP-SAR) predicted that dianioni

183 Here, the development of a predictive QSAR is reported for bacterial biofilm formation on a ra

184 ship (kNN QSAR) method to develop predictive QSAR models for 157 epipodophyllotoxins synthesized prev

185 gorously validated and externally predictive QSAR models.

186 successful development of highly predictive QSAR models affords further design and discovery of nove

187 that the combined application of predictive QSAR modeling and database mining may provide an importa

188 We present QSAR-based regioselectivity models for these enzymes cal

189 ino acids for SARM interactions and provided QSAR data as the basis for mechanistic studies of AR str

190 The derived LOO validated PLS regression QSAR model showed acceptable r(2) 0.92 and q(2) 0.75.

191 uantitative structure-activity relationship (QSAR) analyses.

192 uantitative structure-activity relationship (QSAR) analysis demonstrated excellent correlations with

193 uantitative structure-activity relationship (QSAR) analysis of proteins and peptides.

194 uantitative structure-activity relationship (QSAR) analysis revealed distinct molecular features nece

195 uantitative structure-activity relationship (QSAR) analysis showed that, besides the essential pharma

196 uantitative structure activity relationship (QSAR) and confirmatory studies with synthetic peptides.

197 uantitative Structure-Activity Relationship (QSAR) and Linear Solvation Energy Relationship (LSER) te

198 uantitative structure-activity relationship (QSAR) and structure-activity relationship (SAR) analyses

199 uantitative structure-activity relationship (QSAR) approach based on local molecular properties (Van

200 uantitative structure-activity relationship (QSAR) classification model.

201 uantitative structure-activity relationship (QSAR) equations were thus developed.

202 uantitative structure-activity relationship (QSAR) estimates.

203 uantitative structure-activity relationship (QSAR) method commonly used to predict the physicochemica

204 uantitative structure activity relationship (QSAR) model is established for oligopeptides that inhibi

205 uantitative structure-activity relationship (QSAR) model outputs.

206 uantitative structure-activity relationship (QSAR) model that yielded a cross-validated correlation c

207 uantitative structure-activity relationship (QSAR) model was obtained, showing good predictive models

208 uantitative structure-activity relationship (QSAR) model.

209 uantitative structure-activity relationship (QSAR) model: log 1/I50 = 1.06 B5(2) + 0.33 B5(3) - 0.18p

210 uantitative structure-activity relationship (QSAR) models based on feed-forward neural networks and i

211 uantitative structure activity relationship (QSAR) models built using optimized machine learning tech

212 uantitative structure-activity relationship (QSAR) models for 48 chemically diverse functionalized am

213 uantitative structure-activity relationship (QSAR) models for 48 GGTIs using variable selection k nea

214 uantitative structure-activity relationship (QSAR) models for chemical database mining.

215 uantitative structure-activity relationship (QSAR) models for this data set as unprotonated species a

216 uantitative structure-activity relationship (QSAR) models for three screens of biological activity: l

217 uantitative structure-activity relationship (QSAR) models have been developed for 48 antagonists of t

218 uantitative structure-activity relationship (QSAR) models have been employed to assess nanosafety.

219 uantitative Structure-Activity Relationship (QSAR) models of compounds binding to 5-hydroxytryptamine

220 uantitative structure-activity relationship (QSAR) models to generate in silico ADMET profiles for hi

221 uantitative structure-activity relationship (QSAR) models to predict the bioactivity of those PFASs.

222 uantitative structure-activity relationship (QSAR) models were developed based on ARE-bla data.

223 uantitative structure-activity relationship (QSAR) models.

224 uantitative structure-activity relationship (QSAR) models.

225 uantitative structure-activity relationship (QSAR) models.

226 uantitative structure-activity relationship (QSAR) models.

227 uantitative structure-activity relationship (QSAR) of a group of endogenous and synthetic compounds f

228 uantitative structure-activity relationship (QSAR) of the human concentrative nucleoside transporter

229 uantitative structure-activity relationship (QSAR) platform covering both estrogen receptor (ER) and

230 uantitative structure-activity relationship (QSAR) studies were performed with the comparative molecu

231 uantitative structure-activity relationship (QSAR) study identified the p-tolyl-substituted bifunctio

232 uantitative structure-activity relationship (QSAR) study is presented for quaternary soft anticholine

233 uantitative structure-activity relationship (QSAR) techniques commonly used in drug design.

234 uantitative structure-activity relationship (QSAR) techniques, together with pharmacophore modeling,

235 uantitative structure-activity relationship (QSAR) using previously reported values of log(k) for non

236 uantitative structure-activity relationship (QSAR) was examined using comparative molecular field ana

237 uantitative structure-activity relationship (QSAR), and DNA reductive alkylating agent design.

238 uantitative structure-affinity relationship (QSAR) was derived using triplex binding data for all 14

239 antitative structure-activity relationships (QSAR) modeling, has developed many important algorithms

240 antitative Structure-Activity Relationships (QSAR) models implementing deep learning for predicting a

241 antitative structure activity relationships (QSAR) properties on analogue binding and rates of substr

242 antitative structure activity relationships (QSAR), a common method for pharmaceutical function optim

243 antitative structure-activity relationships (QSAR).

244 e structure-activity/toxicity relationships (QSAR/QSTR) modeling have provided important insights for

245 antitative Structure-Activity Relationships (QSARs) for a range of chemical properties that can be ap

246 antitative structure-activity relationships (QSARs) to predict HLT and HLB from chemical structure an

247 antitative structure-activity relationships (QSARs) use molecular descriptors to predict the bioavail

248 antitative structure-activity relationships (QSARs), but they improved with the specificity of the de

249 antitative structure-activity relationships (QSARs).

250 antitative structure-activity relationships, QSARs) for hydrolysis of other NACs.

251 To test the robustness of the resulting QSAR model, the synthesis of a series of non-peptide thr

252 Resulting QSARs are two-dimensional (2D) fragment-based group cont

253 Reliable and robust QSAR models with good explanatory and predictive propert

254 terms of their potential to generate robust QSAR models.

255 hysicochemical properties specific to the SA QSAR model, while five properties are specific to the MR

256 es were used to build kNN variable selection QSAR models.

257 Several QSAR methods have been employed, including comparative m

258 Two statistically significant QSAR models identified electrostatic interaction as the

259 calculations and encourage species-specific QSAR involving log D(N) and log D(I).

260 A successful QSAR for predicting the lipophilicity (log P(ow)) of sev

261 tion of the MLR model, the ANN, kNN, and SVM QSARs were ensemble models.

262 d the experimental affinity data better than QSAR results.

263 Our results indicate that QSAR models generated using five z descriptors had the h

264 The QSAR development and evaluation method does not require

265 The QSAR exhibited a correlation between the predicted and e

266 The QSAR model is discussed in terms of the exosite model, a

267 The QSAR model was applied to an in-house database including

268 The QSAR models had both high sensitivity (> 75%) and specif

269 The QSAR models were employed for virtual screening of 9.5 m

270 The QSAR provides guidelines for the design of improved trip

271 The QSAR revealed that the primary favorable determinant of

272 The QSAR study shows a good correlation of observed EC(90) f

273 The QSAR was then validated for energetic NACs using newly m

274 Additionally, the QSAR equations developed in the work may serve as a basi

275 These results were analysed by the QSAR method and were in accordance with predicted values

276 xternal test set of 11 Cu(II) complexes, the QSAR preformed with great accuracy; r(2) = 0.93 and a q(

277 tested on three classical data sets from the QSAR literature.

278 On the other hand, the QSAR for the interactions of 27 electron-attracting phen

279 set) and estimates of the uncertainty in the QSAR predictions.

280 SAR models that bound with AC50 < 1 muM, the QSAR models accurately predicted the binding for the par

281 as primary descriptors in development of the QSAR (quantitative structure-activity relationships) of

282 ity and selectivity in the derivation of the QSAR model.

283 Further to the QSAR analysis, 15 peptides were synthesized and tested u

284 The binding strength relative to the QSAR-predicted binding strength was examined for the ER

285 he R2 values for the experimental versus the QSAR-predicted activities were 0.78 or 0.61 for CoMFA an

286 The data show close agreement with the QSAR, supporting its applicability to other energetic NA

287 riven modelling methods professed within the QSAR field can become essential for scientists working b

288 Lien, Ariens, and co-workers based on their QSAR study.

289 Three QSAR models showed that the experimental cell growth inh

290 nge of research areas outside of traditional QSAR boundaries including synthesis planning, nanotechno

291 dsorption of organic compounds by CNTs using QSAR and LSER techniques.

292 chemical accumulation in fish, models using QSAR-estimated biotransformation rates have been develop

293 (i) in vivo BCFs, (ii) BCFs predicted using QSAR-derived biotransformation rates, (iii) BCFs predict

294 Validated QSAR models with R2 > 0.7, (i.e., kNN and SVM) were used

295 with the development of rigorously validated QSAR models obtained with the variable selection k neare

296 s is predicted on the basis of the validated QSAR models using the applicability domain criteria.

297 encourage the use of high quality, validated QSARs for regulatory decision making.

298 . discoideum, we find an experimental versus QSAR predicted pIC(50) R(2) value of 0.94 for 16 bisphos

299 uorine-substituted sulfonamide analog, which QSAR indicated would demonstrate improved inhibition of

300 against hepatitis B virus replication, with QSAR analysis of our results.