ライフサイエンスコーパス: virtual screening

1 logy and provides a resource for large-scale virtual screening.

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

3 inhibitors were constructed and employed for virtual screening.

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

5 ignment of described inhibitors was used for virtual screening.

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

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

8 chemical library screen with computer-based virtual screening.

9 ing new opportunities in cheminformatics and virtual screening.

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

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

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

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

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

15 as effectively selected using ensemble-based virtual screening.

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

17 ukast, identified by molecular docking-based virtual screening.

18 quantify the interactions in structure-based virtual screening.

19 t to simplify molecule library selection 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 In virtual screening a rapid and cost-effective computation

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

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

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

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

28 If virtual screening against homology models of GPCRs could

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

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

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

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

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

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

35 The seminal hit molecule was discovered by virtual screening and confirmed through a series of bioc

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

37 on of ligand bioactivities are essential for virtual screening and drug discovery.

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

39 We used structure-based virtual screening and fragment-based drug discovery to i

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

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

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

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

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

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

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

47 Both inverse virtual screening and saturation transfer difference (ST

48 sure, was identified through structure-based virtual screening and shown to function both as an agoni

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

50 ied a novel class of human sialin ligands by virtual screening and structure-activity relationship st

51 Thus, virtual screening and structure-guided optimization yiel

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

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

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

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

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

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

58 CADD virtual screening applied to 800 000 compounds identifie

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

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

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

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

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

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

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

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

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

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

69 This virtual screening approach was proved to be a promising

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

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

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

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

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

75 ltitarget inhibitors were identified by this virtual screening approach.

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

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

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

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

80 ma cancer cells which were well supported by virtual screening based on ligand binding affinity and m

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

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

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

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

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

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

87 pted to overcome the limitation of in silico virtual screening by applying a robust in silico drug re

88 On this basis, starting from a virtual screening campaign and subsequent structure-base

89 A virtual screening campaign for fragments inhibiting PTR1

90 A virtual screening campaign is presented that led to smal

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

92 tational biology tasks, and for vScreenML in virtual screening campaigns against other protein target

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

94 Following virtual screening, cell monolayers differentiated on mic

95 Virtual screening combined with experimental assays was

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

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

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

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

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

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

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

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

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

105 Docking-based high-throughput virtual screening followed by 16-point screening on micr

106 In a virtual screening for antagonists that exploit differenc

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

108 re, we performed large scale structure-based virtual screening for new ligand chemotypes using recent

109 Here we performed virtual screening for orthosteric and putative allosteri

110 Virtual screening, for example, often substitutes for hi

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

112 Virtual screening has become a major focus of bioactive

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

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

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

116 Structure-based virtual screening has the potential to mitigate these pr

117 Virtual screening has yielded new nonnucleoside AR antag

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

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

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

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

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

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

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

125 Virtual screening identified 21 potential antimicrobial

126 Virtual screening identified a thiourea analogue, compou

127 Virtual screening in a huge collection of virtual combin

128 Virtual screening included multiple conformations of the

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

130 Virtual Screening is an increasingly attractive way to d

131 Virtual screening is becoming a ground-breaking tool for

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

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

134 e size of libraries available for screening, virtual screening is positioned to assume a more promine

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

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

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

138 High-throughput virtual screening led us to identify hit compound 5, end

139 A virtual screening library was designed to target the hig

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

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

142 The virtual screening methodology proved highly successful,

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

144 Virtual screening methods combined with experimental ass

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

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

147 Virtual screening methods seek some active-enriched frac

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

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

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

151 Consensus virtual screening models were generated and validated ut

152 The virtual screening models were successfully employed to d

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

154 Virtual screening of 1.56 million molecules by this mode

155 Finally, virtual screening of 29332 metabolites predicted 146 com

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

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

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

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

160 Then, we applied the best models for a virtual screening of a large database of chemical compou

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

180 Virtual screening of publicly available databases (Bioin

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

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

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

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

185 ptors could be discovered by structure-based virtual screening of the dark chemical matter.

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

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

188 Through virtual screening of the National Cancer Institute Diver

189 Virtual screening of the NCI chemical database using the

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

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

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

193 We identified 100 candidate molecules by virtual screening of ~2 million small molecules for thos

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

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

196 ity to carry out large-scale structure-based virtual screening on computer clusters in an accessible,

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

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

199 pability with a de novo library design and a virtual screening platform modified for covalent ligands

200 Using a virtual screening platform to search novel chemical prob

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

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

203 conformations and chemical clustering in the virtual screening procedure.

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

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

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

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

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

209 Our virtual screening protocol is generally applicable to dr

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

211 A virtual screening protocol with combination of similarit

212 Our results demonstrate that virtual screening provides an efficient means to mine th

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

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

215 fect match substructure search, (iv) re-rank virtual screening results to achieve selectivity for a p

216 The best performing models were next used in virtual screening, retrieving recently patented sweetene

217 Here, we performed structure-based virtual screening (SBVS) at a previously identified TRPV

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

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

220 The virtual screening step comprised the exploration of a ch

221 g the hydrophobic complementarity during the virtual screening step, we identified 5-benzyloxygramine

222 cular dynamics simulations previously to the virtual screening step.

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

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

225 Herein, we report a virtual screening strategy that led to the discovery of

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

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

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

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

230 A virtual screening study aimed at identifying small molec

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

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

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

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

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

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

237 Using virtual screening techniques, we identified a few potent

238 With structure-based virtual screening, the quality of the hits improves with

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

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

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

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

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

244 We performed a virtual screening to identify APSR inhibitors.

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

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

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

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

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

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

251 r kinases relevant to MTC were generated for virtual screening to identify unique preliminary hits th

252 Here, we use covalent virtual screening to produce nano-/picomolar boronic aci

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

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

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

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

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

258 sible approach to finding sweet molecules is virtual screening using compatibility of candidate molec

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

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

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

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

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

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

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

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

267 Virtual Screening (VS) methods can drastically accelerat

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

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

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

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

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

273 Following the outcome of docking-based virtual screening (VS), we synthesized seven novel deriv

274 We proposed MAGELLAN, a novel hierarchical virtual-screening (VS) pipeline, which starts with low-r

275 A computational field known as 'virtual screening' (VS) has emerged in the past decades

276 t, we train a general-purpose classifier for virtual screening (vScreenML) that is built on the XGBoo

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

278 Virtual screening was performed against experimentally e

279 Virtual screening was performed against the NMR model, a

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

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

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

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

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

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

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

289 Finally, using high-throughput virtual screening, we identified P (1),P (5)-di(adenosin

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

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

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

293 A complementary approach is virtual screening, where chemical libraries can be effic

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

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

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

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

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

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

300 To address this limitation, we combined virtual screening, x-ray crystallography, and structure-