ライフサイエンスコーパス: virtual screening

1 sites in the binding pocket is identified by virtual screening.

2 ignment of described inhibitors was used for virtual screening.

3 th a binding mode that was also predicted by virtual screening.

4 r, discovered by ligand- and structure-based virtual screening.

5 chemical library screen with computer-based virtual screening.

6 gh application of structure and ligand-based virtual screening.

7 were identified by means of similarity-based virtual screening.

8 y technologies, including fragment-based and virtual screening.

9 the binding site) followed by docking-based virtual screening.

10 llenges in the application of receptor-based virtual screening.

11 Ligand docking is a widely used approach in virtual screening.

12 dentifying novel bioactive scaffolds through virtual screening.

13 ring of diverse compounds in high-throughput virtual screening.

14 e receptor flexibility in ligand docking and virtual screening.

15 used to define target constraints to assist virtual screening.

16 cking programs is compared in the context of virtual screening.

17 FREDA) to account for protein flexibility in virtual screening.

18 de protein flexibility in ligand docking and virtual screening.

19 otein flexibility in both ligand docking and virtual screening.

20 cophore modeling followed by structure-based virtual screening.

21 knowledge in machine learning to facilitate virtual screening.

22 a combination of structure- and ligand-based virtual screening.

23 quantify the interactions in structure-based virtual screening.

24 e ready for structure-based and ligand-based virtual screening.

25 inhibitors were constructed and employed for virtual screening.

26 In virtual screening a rapid and cost-effective computation

27 The observations highlight the utility of virtual screening against a comparative model, even when

28 th genistein, computer-aided structure-based virtual screening against a natural source chemical data

29 red and evaluated for their effectiveness in virtual screening against a wide variety of protein targ

30 Here, we use virtual screening against an ensemble of both crystal st

31 small molecule derived from structure-based virtual screening against erWalK is capable of selective

32 If virtual screening against homology models of GPCRs could

33 Ensemble-based virtual screening against this newly revealed pocket sel

34 y models that are accurate enough for simple virtual screening aimed at computer-aided drug discovery

35 ted antagonist-bound conformation and used a virtual screening algorithm to select 100 TR antagonist

36 Discovered by structure-based virtual screening algorithms, bafetinib, a Bcr-Abl/Lyn t

37 mpound collection was evaluated by consensus virtual screening and a hit was identified.

38 Through structure-based virtual screening and cell-based assays, we discovered a

39 Through virtual screening and cell-based assays, we report here

40 e updated homology models will be useful for virtual screening and drug design.

41 Using a combination of virtual screening and experimental testing, novel small

42 With this modified version of the program, virtual screening and further docking-based optimization

43 In this study, rapid structure-based virtual screening and hit-based substructure search were

44 ation" with chemical intuition (or bias) for virtual screening and lead optimization but also has its

45 Starting from a sequential structure-based virtual screening and medicinal chemistry strategy, we i

46 of M. tuberculosis GlgB for high throughput virtual screening and molecular docking.

47 al design of ligands and for high-throughput virtual screening and offer competitive performance to m

48 "lead hopping", using topomer similarity for virtual screening and queries from the patent literature

49 icability of active-state GPCR structures to virtual screening and rational optimization of agonists,

50 d lead discovery (FBLD) by NMR combined with virtual screening and re-mining of biochemical high-thro

51 Both inverse virtual screening and saturation transfer difference (ST

52 resented here can also serve as a target for virtual screening and soaking studies of small molecules

53 potency by a combination of similarity-based virtual screening and subsequent synthetic optimization

54 eterminants of novel receptors, to assist in virtual screening and to design and optimize drug candid

55 , we report the lead identification through "virtual screening" and the synthesis of our first series

56 ly novel substrate, by comparative modeling, virtual screening, and experimental validation.

57 ability simulations, pharmacophore modeling, virtual screening, and in vitro fluorescence measurement

58 associated with large-scale high-throughput virtual screening, and provides a convenient and efficie

59 g, ligand-support binding site optimization, virtual screening, and structure clustering analysis, wa

60 ng protein flexibility in ligand docking and virtual screening, and to validate the merging and shrin

61 combinatorial chemistry, high-throughput and virtual screening, and traditional medicinal chemistry,

62 CADD virtual screening applied to 800 000 compounds identifie

63 In parallel, an inverse virtual screening approach aimed at identifying protein

64 operties, we applied a model structure-based virtual screening approach augmented by chemical similar

65 We describe a combinatorial virtual screening approach for predicting high specifici

66 This virtual screening approach improved the physical screeni

67 We employed an ensemble structure-based virtual screening approach in combination with a multipl

68 The described virtual screening approach may prove applicable in the s

69 able and integrated target-specific "tiered" virtual screening approach tailored to identifying and c

70 This work provides a validated virtual screening approach that is applicable to other B

71 ined ligand-based and target structure-based virtual screening approach that took into account the kn

72 We describe a virtual screening approach to identify BRD inhibitors.

73 In the present work, we describe a virtual screening approach to identify inhibitors of S.

74 This virtual screening approach was proved to be a promising

75 Therefore, we combined a structure-based virtual screening approach with density functional theor

76 Using a virtual screening approach, a series of benzopyran compo

77 elomeric sequence employing a receptor-based virtual screening approach.

78 dylate synthase (hTS) was targeted through a virtual screening approach.

79 Herein, we selected 16 compounds through a virtual screening approach.

80 l molecule ligands for these receptors using virtual screening approaches based on proteochemometric

81 Here, using virtual screening approaches, we identify 11 CARM1 (PRMT

82 king step, where the results of the multiple virtual screenings are condensed to improve the enrichme

83 These results support virtual screening as an effective tool for discovery of

84 Phenothiazines were identified by virtual screening as promising ligands for HIV-1 TAR RNA

85 itors of this interaction were identified by virtual screening based on available structures with use

86 We propose a new method for virtual screening based on protein interaction profile s

87 ding algorithm for genome-scale multi-target virtual screening based on the one-class collaborative f

88 discover analgesic drugs via structure-based virtual screening based on the recently published NMR st

89 ibrium property is surprisingly effective in virtual screening because true ligands form more-resilie

90 cedure, we compiled an extensive small-scale virtual screening benchmark of 33 crystal structures of

91 hthyl salicylic acyl hydrazone (NSAH), using virtual screening, binding affinity, inhibition, and cel

92 Using virtual screening, binding affinity, inhibition, and cel

93 A virtual screening campaign for fragments inhibiting PTR1

94 A virtual screening campaign is presented that led to smal

95 The prospective virtual screening campaign yielded a high 32% hit rate,

96 This study sets the basis for future virtual screening campaigns targeting the five novel reg

97 ntagonist compounds shows how receptor-based virtual screening can identify diverse chemistries that

98 Following virtual screening, cell monolayers differentiated on mic

99 Virtual screening combined with experimental assays was

100 mance has been observed for similarity-based virtual screening compared to structure-based methods.

101 various hit identification tasks, including virtual screening, compound repurposing, and the detecti

102 etween i6A and FPPS, we undertook an inverse virtual screening computational target searching, testin

103 pose predictions and ranking compounds in a virtual screening context.

104 Particular emphasis is placed on virtual screening, de novo design, evaluation of drug-li

105 mproved, since the ligands considered in the virtual screening docked within 1.5 A to at least one of

106 We then predicted through virtual screening dozens of potential inhibitors for sev

107 Homology model-based virtual screening, especially with modeling of protein b

108 ssible to learn from a formally unsuccessful virtual-screening exercise and, with the aid of computat

109 onsensus scoring enhances the hit rates in a virtual screening experiment.

110 st an extended set of 63 cocrystals and in a virtual screening experiment.

111 ctive scaffolds in compound similarity-based virtual screening experiments has been studied comparing

112 imera extension used to visualize results of virtual screening experiments.

113 eptor docking in binding-mode prediction and virtual screening experiments.

114 the high-throughput screening facilities and virtual screening facilities we have implemented for ide

115 In a virtual screening for antagonists that exploit differenc

116 es for three tasks: binding mode prediction, virtual screening for lead identification, and rank-orde

117 Here we performed virtual screening for orthosteric and putative allosteri

118 Virtual screening, for example, often substitutes for hi

119 It was identified by virtual screening from a NCI small molecule library, but

120 Virtual screening has become a major focus of bioactive

121 bitors of the tautomerase activity of PfMIF, virtual screening has been performed by docking 2.1 mill

122 he design through energy-based pharmacophore virtual screening has led to aminocyanopyridine derivati

123 omic-resolution information, structure-based virtual screening has rarely been used to drive fragment

124 Virtual screening has yielded new nonnucleoside AR antag

125 Although many methods for single-target virtual screening have been developed to improve the eff

126 of hit identification criteria, and general virtual screening hit criteria to allow for realistic hi

127 Structural modification of a virtual screening hit led to the identification of a new

128 ategy to guide lead optimization, a 5 microM virtual screening hit was transformed to a series of ver

129 From the virtual screening hits, 29 substances were evaluated in

130 We survey low cost high-throughput virtual screening (HTVS) computer programs for instructo

131 Here, we report a high-throughput virtual screening (HTVS) study using phosphoinositide 3-

132 Virtual screening identified a thiourea analogue, compou

133 A second round of virtual screening identified new compounds with predicte

134 Virtual screening in a huge collection of virtual combin

135 Virtual screening included multiple conformations of the

136 Virtual screening included scoring normalization procedu

137 Molecular-docking-based virtual screening is an important tool in drug discovery

138 Virtual Screening is an increasingly attractive way to d

139 Virtual screening is becoming a ground-breaking tool for

140 e shown that the effectiveness of docking in virtual screening is highly variable due to a large numb

141 Virtual screening is one of the major tools used in comp

142 er class of method that has shown promise in virtual screening is the shape-based, ligand-centric app

143 ng in a cellular model of viral latency with virtual screening is useful for the identification of no

144 approach, which combines fragment based and virtual screening, is rapid and cost effective and can b

145 A method for ligand-based virtual screening (LBVS), dynamic mapping of consensus p

146 ikeness score, that finds its application in virtual screening, library design and compound selection

147 gand overlap score (xLOS), a 3D ligand-based virtual screening method recently developed in our group

148 The virtual screening methodology proved highly successful,

149 This study used in silico virtual screening methodology to identify several nonhyd

150 Virtual screening methods combined with experimental ass

151 Accordingly, virtual screening methods combined with experimental ass

152 a powerful tool to assess the performance of virtual screening methods on NRs, to assist the understa

153 This protocol covers the docking and virtual screening methods provided by the AutoDock suite

154 The combination of pharmacophore-based virtual screening methods with radioactive methylation a

155 lass of azetidinone CB1 antagonists by using virtual screening methods.

156 used 12 UPPS crystal structures to validate virtual screening models and then assayed 100 virtual hi

157 Consensus virtual screening models were generated and validated ut

158 The virtual screening models were successfully employed to d

159 According to this pattern, a ligand-based virtual screening of 1 444 880 active compounds from Chi

160 Finally, virtual screening of 29332 metabolites predicted 146 com

161 Following the identification through virtual screening of 4-(2,4-dimethyl-thiazol-5-yl)pyrimi

162 The virtual screening of 68,752 natural compounds via molecu

163 The QSAR models were employed for virtual screening of 9.5 million commercially available

164 Using crystallographic data and the virtual screening of a chemical library, we identified a

165 r further analysis, or used as the basis for virtual screening of a compound database uploaded by the

166 The structure-based virtual screening of a large commercial chemical compoun

167 for Smo agonists and used this model for the virtual screening of a library of commercially available

168 entified previously by homology modeling and virtual screening of a library of small molecules.

169 melitannin) as a nonpeptide analog of RIP by virtual screening of a RIP-based pharmacophore against a

170 gands for CYP11B2 and the related CYP11B1, a virtual screening of a small compounds library of our ea

171 nd-optimized structural templates to perform virtual screening of available compound libraries for ne

172 sensus protocol was developed for use in the virtual screening of chemical databases, focused toward

173 ivity Relationship models and using them for virtual screening of chemical libraries to prioritize th

174 By virtual screening of chemicals that fit into a surface g

175 armacophore model which could be used in the virtual screening of compound collections and potentiall

176 A pharmacophore and docking-based virtual screening of compound libraries led to the selec

177 ion of therapeutic leads include chemical or virtual screening of compound libraries.

178 NSC23766 was identified by a structure-based virtual screening of compounds that fit into a surface g

179 er, has been modified to carry out automated virtual screening of covalent inhibitors.

180 sites, but at present this should allow for virtual screening of drug libraries at these putative in

181 Virtual screening of drugs, metabolites, fragments-like,

182 homology models we derived, high-throughput virtual screening of five million compounds resulted in

183 s an online, interactive environment for the virtual screening of large compound databases using phar

184 The methods are fast enough to allow virtual screening of ligand libraries containing tens of

185 nti-HCV agents, we performed structure-based virtual screening of our in-house library followed by ra

186 Thus, we performed a virtual screening of over one million compounds using DO

187 Virtual screening of publicly available databases (Bioin

188 nnabinoid target-biased library generated by virtual screening of sample collections using a pharmaco

189 Docking tools are largely used for virtual screening of small drug-like molecules, but thei

190 stal structure, we performed structure-based virtual screening of small-molecule libraries to seek in

191 ive compound identified through an in silico virtual screening of the Chinese Medicine Library.

192 Virtual screening of the human AICAR transformylase acti

193 Virtual screening of the Maybridge library of ca. 70 000

194 ound targeting this pocket was identified by virtual screening of the National Cancer Institute (NCI)

195 Through virtual screening of the National Cancer Institute Diver

196 Virtual screening of the NCI chemical database using the

197 resents a signature for the experimental and virtual screening of therapeutic antagonists that target

198 s of RNA molecules and (iii) high-throughput virtual screening of this library to select aptamers wit

199 Structure-based virtual screening of two libraries containing 567981 mol

200 ablish this proof-of-principle, we performed virtual screening on a library of >70,000 commercially a

201 mpt us to identify, through a receptor-based virtual screening on an in house database, dual MDM2/MDM

202 Using structure-based virtual screening, our group recently identified a novel

203 uation of a protein-based and a ligand-based virtual screening platform against a set of three G-prot

204 Using a virtual screening platform to search novel chemical prob

205 A two-step, fully automatic virtual screening procedure consisting of flexible docki

206 Compounds selected by the virtual screening procedure were then tested for their a

207 Here, we have performed a novel virtual screening procedure, depending on ligand-based p

208 conformations and chemical clustering in the virtual screening procedure.

209 Herein, we describe the virtual screening process involving feature trees fragme

210 From this virtual screening process, 81 were tested for inhibition

211 ating between binders and non-binders in the virtual screening process.

212 and external data indicate a high value for virtual screening projects.

213 inum neurotoxin subtype A (BoNT/A) using the virtual screening protocol "protein scanning with virtua

214 ovel BChE inhibitors, we used a hierarchical virtual screening protocol followed by biochemical evalu

215 Our virtual screening protocol is generally applicable to dr

216 for novel NK(3) receptor antagonists using a virtual screening protocol of similarity analysis.

217 Users can import, create and edit virtual screening queries in an interactive browser-base

218 Importantly, our virtual screening results demonstrate superior enrichmen

219 A critical analysis of virtual screening results published between 2007 and 201

220 based drug design (SBDD) and structure-based virtual screening (SBVS).

221 Compound 1 (IC50 = 711 nM), selected by virtual screening, showed inhibitory activity toward TDO

222 ated structures were used in the small-scale virtual screening stage and, by merging and shrinking th

223 The virtual screening step comprised the exploration of a ch

224 cular dynamics simulations previously to the virtual screening step.

225 These results indicate that virtual screening strategies can be successfully applied

226 A multistage virtual screening strategy designed so as to overcome kn

227 A structure-based virtual screening strategy, comprising homology modeling

228 Using a computational virtual screening strategy, we have identified a unique

229 This approach includes virtual screening (structure- and ligand-based design) a

230 In a series of virtual screening studies involving 9969 MDDR compounds

231 A virtual screening study aimed at identifying small molec

232 Computational genetic algorithm-based virtual screening study shows that only one sulfated DHP

233 on using three iterations of library design, virtual screening, synthesis, and biological testing.

234 This article describes design, virtual screening, synthesis, and biological tests of no

235 the pterin binding pocket, we have performed virtual screening, synthetic, and structural studies usi

236 that the designed DT models can be used as a virtual screening technique as well as a complement to t

237 p38 inhibitors, we applied the ligand-based virtual screening technique, FieldScreen, to 1.2 million

238 hroughput screening, fragment screening, and virtual screening techniques and characterized by enzyme

239 Using virtual screening techniques, we identified a few potent

240 ies of MCH-1R antagonists using a variety of virtual screening techniques.

241 In structure-based virtual screening, the scoring function is critical to i

242 say that any one structure is "the best" for virtual screening, there are some structures that are cl

243 lamino)acetamide hydrochloride (PJ34), using virtual screening; this inhibitor reduced the N protein'

244 ng domain (DBD) of the receptor and utilized virtual screening to discover a set of micromolar hits f

245 ndings support the feasibility of the use of virtual screening to discover allosteric modulators of p

246 We performed a virtual screening to identify APSR inhibitors.

247 Here we used protein structure analysis and virtual screening to identify drug-like molecules that b

248 Here we performed an expression-based virtual screening to identify highly active antileukemic

249 ion enhancers, and employed these models for virtual screening to identify putative 5-HT(6)R actives.

250 study demonstrates the feasibility of using virtual screening to identify small molecules that are s

251 We report hybrid structure-based virtual screening to identify small molecules with the p

252 nding affinity prediction and the ability of virtual screening to identify true binders in chemical l

253 dipitous process, we employed 3D shape-based virtual screening to reprofile existing FDA-approved dru

254 eveloped in this study as a 3D query tool in virtual screening to retrieve new chemical entities as p

255 problem in HTS data and it can be used as a virtual screening tool to identify potential interferenc

256 As a consequence, modern virtual screening tools developed to identify inhibitors

257 developed QSAR models can serve as reliable virtual screening tools, leading to the discovery of str

258 Virtual screening uses computer-based methods to discove

259 By virtual screening using a fragment-based drug design (FB

260 used in this study were identified through a virtual screening using HIV-reverse transcriptase (RT),

261 ions thus obtained are used in a small-scale virtual screening using receptor ensemble docking.

262 of self-docked complexes, and enrichment in virtual screening, using a large data set of PDB complex

263 Lead generation began with directed virtual screening, using a ligand-based focused library

264 edicted and validated structure of CCR1 in a virtual screening validation of the Maybridge data base,

265 Although virtual screening (VS) against a crystal or relaxed rece

266 w open-source ligand structure alignment and virtual screening (VS) algorithm, LIGSIFT, that uses Gau

267 has relied on a ligand 3-D shape similarity virtual screening (VS) approach using the ROCS program a

268 new ligands for this receptor, we performed virtual screening (VS) based on two-dimensional (2D) sim

269 In large-scale virtual screening (VS) campaigns, data are often compute

270 e predictiveness in tier-based approaches to virtual screening (VS) have mainly focused on protein ki

271 interest in using structural information for virtual screening (VS) of libraries and for structure-ba

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

273 A structure-based virtual screening (VS) protocol was developed with the i

274 Based on extensive virtual screening (VS) tests, the flexible matching feat

275 tive measure of affinity that can be used in virtual screening (VS) to rank order a list of compounds

276 using a two-tailed T test, demonstrated that virtual screening was able to predict reliably the sensi

277 Virtual screening was performed against experimentally e

278 Virtual screening was performed against the NMR model, a

279 Structure-based virtual screening was performed against the target dipep

280 rse inhibitors of Cdc25 biological activity, virtual screening was performed by docking 2.1 million c

281 ule inhibitors of MIF's biological activity, virtual screening was performed by docking 2.1 million c

282 Virtual screening was performed in an attempt to identif

283 Iterative pharmacophore-based virtual screening was performed to identify druglike mol

284 Starting from known c-Src inhibitors, a virtual screening was performed to identify molecules ab

285 discover more potent and diverse inhibitors, virtual screening was performed.

286 Virtual screening was used to identify potential inhibit

287 As a result of our exhaustive virtual screening, we biochemically validated novel pote

288 tion studies and pharmacophore-docking-based virtual screening, we discovered a series of dihydrodibe

289 Using structure-based virtual screening, we identified a compound predicted to

290 Now, using virtual screening, we identified a subset of small molec

291 By using virtual screening, we identified small ligands of ARTD7

292 Using structure-based virtual screening, we previously identified a novel stil

293 igorous experimental screening and in silico virtual screening, we recently identified novel classes

294 rical screening, have re-ignited interest in virtual screening, which is now widely used in drug disc

295 chemical libraries by using structure-based virtual screening with a computer model of the Stat3 SH2

296 Subsequently, a virtual screening with a library of 28341 compounds iden

297 tes a multi-target predictor for large scale virtual screening with potential in lead discovery, repo

298 We integrated structure-based virtual screening with the chemical genomics analysis of

299 ticle, the implementation of a docking-based virtual screening workflow for the retrieval of covalent

300 enrichment factors, compared with the Glide virtual screening workflow.