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Items: 1 to 20 of 97

1.

CSAR data set release 2012: ligands, affinities, complexes, and docking decoys.

Dunbar JB Jr, Smith RD, Damm-Ganamet KL, Ahmed A, Esposito EX, Delproposto J, Chinnaswamy K, Kang YN, Kubish G, Gestwicki JE, Stuckey JA, Carlson HA.

J Chem Inf Model. 2013 Aug 26;53(8):1842-52. doi: 10.1021/ci4000486. Epub 2013 May 10.

2.

CSAR benchmark exercise 2011-2012: evaluation of results from docking and relative ranking of blinded congeneric series.

Damm-Ganamet KL, Smith RD, Dunbar JB Jr, Stuckey JA, Carlson HA.

J Chem Inf Model. 2013 Aug 26;53(8):1853-70. doi: 10.1021/ci400025f. Epub 2013 May 10.

3.

Automated large-scale file preparation, docking, and scoring: evaluation of ITScore and STScore using the 2012 Community Structure-Activity Resource benchmark.

Grinter SZ, Yan C, Huang SY, Jiang L, Zou X.

J Chem Inf Model. 2013 Aug 26;53(8):1905-14. doi: 10.1021/ci400045v. Epub 2013 May 21.

4.

Solvated interaction energy (SIE) for scoring protein-ligand binding affinities. 2. Benchmark in the CSAR-2010 scoring exercise.

Sulea T, Cui Q, Purisima EO.

J Chem Inf Model. 2011 Sep 26;51(9):2066-81. doi: 10.1021/ci2000242. Epub 2011 Jul 13.

PMID:
21714553
5.

CSAR benchmark exercise of 2010: selection of the protein-ligand complexes.

Dunbar JB Jr, Smith RD, Yang CY, Ung PM, Lexa KW, Khazanov NA, Stuckey JA, Wang S, Carlson HA.

J Chem Inf Model. 2011 Sep 26;51(9):2036-46. doi: 10.1021/ci200082t. Epub 2011 Jul 22. Erratum in: J Chem Inf Model. 2011Sep 26;51(9):2146.

6.

SFCscore(RF): a random forest-based scoring function for improved affinity prediction of protein-ligand complexes.

Zilian D, Sotriffer CA.

J Chem Inf Model. 2013 Aug 26;53(8):1923-33. doi: 10.1021/ci400120b. Epub 2013 Jun 10.

PMID:
23705795
7.

Predicting binding affinity of CSAR ligands using both structure-based and ligand-based approaches.

Fourches D, Muratov E, Ding F, Dokholyan NV, Tropsha A.

J Chem Inf Model. 2013 Aug 26;53(8):1915-22. doi: 10.1021/ci400216q. Epub 2013 Jul 17.

8.

Docking and Scoring with Target-Specific Pose Classifier Succeeds in Native-Like Pose Identification But Not Binding Affinity Prediction in the CSAR 2014 Benchmark Exercise.

Politi R, Convertino M, Popov K, Dokholyan NV, Tropsha A.

J Chem Inf Model. 2016 Jun 27;56(6):1032-41. doi: 10.1021/acs.jcim.5b00751. Epub 2016 Apr 20.

PMID:
27050767
9.

A New Scoring Function for Molecular Docking Based on AutoDock and AutoDock Vina.

Tanchuk VY, Tanin VO, Vovk AI, Poda G.

Curr Drug Discov Technol. 2015;12(3):170-8.

PMID:
26302746
10.

Construction and test of ligand decoy sets using MDock: community structure-activity resource benchmarks for binding mode prediction.

Huang SY, Zou X.

J Chem Inf Model. 2011 Sep 26;51(9):2107-14. doi: 10.1021/ci200080g. Epub 2011 Aug 3.

11.

Lessons learned in empirical scoring with smina from the CSAR 2011 benchmarking exercise.

Koes DR, Baumgartner MP, Camacho CJ.

J Chem Inf Model. 2013 Aug 26;53(8):1893-904. doi: 10.1021/ci300604z. Epub 2013 Feb 12.

12.

Target-specific native/decoy pose classifier improves the accuracy of ligand ranking in the CSAR 2013 benchmark.

Fourches D, Politi R, Tropsha A.

J Chem Inf Model. 2015 Jan 26;55(1):63-71. doi: 10.1021/ci500519w. Epub 2014 Dec 18.

PMID:
25521713
13.

CSAR 2014: A Benchmark Exercise Using Unpublished Data from Pharma.

Carlson HA, Smith RD, Damm-Ganamet KL, Stuckey JA, Ahmed A, Convery MA, Somers DO, Kranz M, Elkins PA, Cui G, Peishoff CE, Lambert MH, Dunbar JB Jr.

J Chem Inf Model. 2016 Jun 27;56(6):1063-77. doi: 10.1021/acs.jcim.5b00523. Epub 2016 May 17.

14.

Choosing the Optimal Rigid Receptor for Docking and Scoring in the CSAR 2013/2014 Experiment.

Baumgartner MP, Camacho CJ.

J Chem Inf Model. 2016 Jun 27;56(6):1004-12. doi: 10.1021/acs.jcim.5b00338. Epub 2015 Aug 7.

15.

CSAR Benchmark Exercise 2013: Evaluation of Results from a Combined Computational Protein Design, Docking, and Scoring/Ranking Challenge.

Smith RD, Damm-Ganamet KL, Dunbar JB Jr, Ahmed A, Chinnaswamy K, Delproposto JE, Kubish GM, Tinberg CE, Khare SD, Dou J, Doyle L, Stuckey JA, Baker D, Carlson HA.

J Chem Inf Model. 2016 Jun 27;56(6):1022-31. doi: 10.1021/acs.jcim.5b00387. Epub 2015 Oct 9.

PMID:
26419257
16.

A molecular mechanics approach to modeling protein-ligand interactions: relative binding affinities in congeneric series.

Rapp C, Kalyanaraman C, Schiffmiller A, Schoenbrun EL, Jacobson MP.

J Chem Inf Model. 2011 Sep 26;51(9):2082-9. doi: 10.1021/ci200033n. Epub 2011 Aug 9.

17.

Incorporating backbone flexibility in MedusaDock improves ligand-binding pose prediction in the CSAR2011 docking benchmark.

Ding F, Dokholyan NV.

J Chem Inf Model. 2013 Aug 26;53(8):1871-9. doi: 10.1021/ci300478y. Epub 2012 Dec 24.

PMID:
23237273
18.

Integration of Ligand and Structure Based Approaches for CSAR-2014.

Prathipati P, Mizuguchi K.

J Chem Inf Model. 2016 Jun 27;56(6):974-87. doi: 10.1021/acs.jcim.5b00477. Epub 2015 Nov 5.

PMID:
26492437
19.

Predicting the biological activities through QSAR analysis and docking-based scoring.

Vilar S, Costanzi S.

Methods Mol Biol. 2012;914:271-84. doi: 10.1007/978-1-62703-023-6_16.

20.

Combined application of cheminformatics- and physical force field-based scoring functions improves binding affinity prediction for CSAR data sets.

Hsieh JH, Yin S, Liu S, Sedykh A, Dokholyan NV, Tropsha A.

J Chem Inf Model. 2011 Sep 26;51(9):2027-35. doi: 10.1021/ci200146e. Epub 2011 Aug 30.

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