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Items: 1 to 20 of 135

1.

Evaluation and optimization of virtual screening workflows with DEKOIS 2.0--a public library of challenging docking benchmark sets.

Bauer MR, Ibrahim TM, Vogel SM, Boeckler FM.

J Chem Inf Model. 2013 Jun 24;53(6):1447-62. doi: 10.1021/ci400115b. Epub 2013 Jun 12.

PMID:
23705874
2.

DEKOIS: demanding evaluation kits for objective in silico screening--a versatile tool for benchmarking docking programs and scoring functions.

Vogel SM, Bauer MR, Boeckler FM.

J Chem Inf Model. 2011 Oct 24;51(10):2650-65. doi: 10.1021/ci2001549. Epub 2011 Aug 18.

PMID:
21774552
3.

Applying DEKOIS 2.0 in structure-based virtual screening to probe the impact of preparation procedures and score normalization.

Ibrahim TM, Bauer MR, Boeckler FM.

J Cheminform. 2015 May 20;7:21. doi: 10.1186/s13321-015-0074-6. eCollection 2015.

4.

Use of experimental design to optimize docking performance: the case of LiGenDock, the docking module of LiGen, a new de novo design program.

Beato C, Beccari AR, Cavazzoni C, Lorenzi S, Costantino G.

J Chem Inf Model. 2013 Jun 24;53(6):1503-17. doi: 10.1021/ci400079k. Epub 2013 Apr 30.

PMID:
23590204
5.

pROC-Chemotype Plots Enhance the Interpretability of Benchmarking Results in Structure-Based Virtual Screening.

Ibrahim TM, Bauer MR, Dörr A, Veyisoglu E, Boeckler FM.

J Chem Inf Model. 2015 Nov 23;55(11):2297-307. doi: 10.1021/acs.jcim.5b00475. Epub 2015 Oct 21.

PMID:
26434782
6.

PSOVina: The hybrid particle swarm optimization algorithm for protein-ligand docking.

Ng MC, Fong S, Siu SW.

J Bioinform Comput Biol. 2015 Jun;13(3):1541007. doi: 10.1142/S0219720015410073. Epub 2015 Feb 10.

PMID:
25800162
7.

LEADS-PEP: A Benchmark Data Set for Assessment of Peptide Docking Performance.

Hauser AS, Windshügel B.

J Chem Inf Model. 2016 Jan 25;56(1):188-200. doi: 10.1021/acs.jcim.5b00234. Epub 2016 Jan 11.

PMID:
26651532
8.

PoLi: A Virtual Screening Pipeline Based on Template Pocket and Ligand Similarity.

Roy A, Srinivasan B, Skolnick J.

J Chem Inf Model. 2015 Aug 24;55(8):1757-70. doi: 10.1021/acs.jcim.5b00232. Epub 2015 Aug 12.

9.

GalaxyDock2: protein-ligand docking using beta-complex and global optimization.

Shin WH, Kim JK, Kim DS, Seok C.

J Comput Chem. 2013 Nov 15;34(30):2647-56. doi: 10.1002/jcc.23438. Epub 2013 Sep 24.

PMID:
24108416
10.

Lead finder: an approach to improve accuracy of protein-ligand docking, binding energy estimation, and virtual screening.

Stroganov OV, Novikov FN, Stroylov VS, Kulkov V, Chilov GG.

J Chem Inf Model. 2008 Dec;48(12):2371-85. doi: 10.1021/ci800166p.

PMID:
19007114
11.

Maximum unbiased validation (MUV) data sets for virtual screening based on PubChem bioactivity data.

Rohrer SG, Baumann K.

J Chem Inf Model. 2009 Feb;49(2):169-84. doi: 10.1021/ci8002649.

PMID:
19434821
12.

FLAP: GRID molecular interaction fields in virtual screening. validation using the DUD data set.

Cross S, Baroni M, Carosati E, Benedetti P, Clementi S.

J Chem Inf Model. 2010 Aug 23;50(8):1442-50. doi: 10.1021/ci100221g.

PMID:
20690627
13.

Structure-based virtual screening approach for discovery of covalently bound ligands.

Toledo Warshaviak D, Golan G, Borrelli KW, Zhu K, Kalid O.

J Chem Inf Model. 2014 Jul 28;54(7):1941-50. doi: 10.1021/ci500175r. Epub 2014 Jun 26.

PMID:
24932913
14.

Systematic and efficient side chain optimization for molecular docking using a cheapest-path procedure.

Schumann M, Armen RS.

J Comput Chem. 2013 May 30;34(14):1258-69. doi: 10.1002/jcc.23251. Epub 2013 Feb 19.

PMID:
23420703
15.

RADER: a RApid DEcoy Retriever to facilitate decoy based assessment of virtual screening.

Wang L, Pang X, Li Y, Zhang Z, Tan W.

Bioinformatics. 2017 Apr 15;33(8):1235-1237. doi: 10.1093/bioinformatics/btw783.

PMID:
28011765
16.

Challenges, applications, and recent advances of protein-ligand docking in structure-based drug design.

Grinter SZ, Zou X.

Molecules. 2014 Jul 11;19(7):10150-76. doi: 10.3390/molecules190710150. Review.

17.

CovalentDock: automated covalent docking with parameterized covalent linkage energy estimation and molecular geometry constraints.

Ouyang X, Zhou S, Su CT, Ge Z, Li R, Kwoh CK.

J Comput Chem. 2013 Feb 5;34(4):326-36. doi: 10.1002/jcc.23136. Epub 2012 Oct 4.

PMID:
23034731
18.

LiGen: a high performance workflow for chemistry driven de novo design.

Beccari AR, Cavazzoni C, Beato C, Costantino G.

J Chem Inf Model. 2013 Jun 24;53(6):1518-27. doi: 10.1021/ci400078g. Epub 2013 May 28.

PMID:
23617275
19.

Benchmark data sets for structure-based computational target prediction.

Schomburg KT, Rarey M.

J Chem Inf Model. 2014 Aug 25;54(8):2261-74. doi: 10.1021/ci500131x. Epub 2014 Aug 1.

PMID:
25084060
20.

Best of both worlds: on the complementarity of ligand-based and structure-based virtual screening.

Broccatelli F, Brown N.

J Chem Inf Model. 2014 Jun 23;54(6):1634-41. doi: 10.1021/ci5001604. Epub 2014 May 30.

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