239 related articles for article (PubMed ID: 26252196)
1. CSAR Benchmark of Flexible MedusaDock in Affinity Prediction and Nativelike Binding Pose Selection.
Nedumpully-Govindan P; Jemec DB; Ding F
J Chem Inf Model; 2016 Jun; 56(6):1042-52. PubMed ID: 26252196
[TBL] [Abstract][Full Text] [Related]
2. 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; 56(6):1032-41. PubMed ID: 27050767
[TBL] [Abstract][Full Text] [Related]
3. 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; 53(8):1871-9. PubMed ID: 23237273
[TBL] [Abstract][Full Text] [Related]
4. 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; 55(1):63-71. PubMed ID: 25521713
[TBL] [Abstract][Full Text] [Related]
5. Boosted neural networks scoring functions for accurate ligand docking and ranking.
Ashtawy HM; Mahapatra NR
J Bioinform Comput Biol; 2018 Apr; 16(2):1850004. PubMed ID: 29495922
[TBL] [Abstract][Full Text] [Related]
6. 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; 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; 56(6):1022-31. PubMed ID: 26419257
[TBL] [Abstract][Full Text] [Related]
7. CSAR 2014: A Benchmark Exercise Using Unpublished Data from Pharma.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
J Chem Inf Model; 2016 Jun; 56(6):1063-77. PubMed ID: 27149958
[TBL] [Abstract][Full Text] [Related]
8. Combined Approach of Patch-Surfer and PL-PatchSurfer for Protein-Ligand Binding Prediction in CSAR 2013 and 2014.
Zhu X; Shin WH; Kim H; Kihara D
J Chem Inf Model; 2016 Jun; 56(6):1088-99. PubMed ID: 26691286
[TBL] [Abstract][Full Text] [Related]
9. Blind Pose Prediction, Scoring, and Affinity Ranking of the CSAR 2014 Dataset.
Martiny VY; Martz F; Selwa E; Iorga BI
J Chem Inf Model; 2016 Jun; 56(6):996-1003. PubMed ID: 26391724
[TBL] [Abstract][Full Text] [Related]
10. Cross-docking benchmark for automated pose and ranking prediction of ligand binding.
Wierbowski SD; Wingert BM; Zheng J; Camacho CJ
Protein Sci; 2020 Jan; 29(1):298-305. PubMed ID: 31721338
[TBL] [Abstract][Full Text] [Related]
11. Predicting Binding Poses and Affinities in the CSAR 2013-2014 Docking Exercises Using the Knowledge-Based Convex-PL Potential.
Grudinin S; Popov P; Neveu E; Cheremovskiy G
J Chem Inf Model; 2016 Jun; 56(6):1053-62. PubMed ID: 26569136
[TBL] [Abstract][Full Text] [Related]
12. 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; 56(6):1004-12. PubMed ID: 26222931
[TBL] [Abstract][Full Text] [Related]
13. Evaluation of the Wilma-SIE Virtual Screening Method in Community Structure-Activity Resource 2013 and 2014 Blind Challenges.
Hogues H; Sulea T; Purisima EO
J Chem Inf Model; 2016 Jun; 56(6):955-64. PubMed ID: 26282162
[TBL] [Abstract][Full Text] [Related]
14. Evaluation of GalaxyDock Based on the Community Structure-Activity Resource 2013 and 2014 Benchmark Studies.
Shin WH; Lee GR; Seok C
J Chem Inf Model; 2016 Jun; 56(6):988-95. PubMed ID: 26583962
[TBL] [Abstract][Full Text] [Related]
15. Improving docking results via reranking of ensembles of ligand poses in multiple X-ray protein conformations with MM-GBSA.
Greenidge PA; Kramer C; Mozziconacci JC; Sherman W
J Chem Inf Model; 2014 Oct; 54(10):2697-717. PubMed ID: 25266271
[TBL] [Abstract][Full Text] [Related]
16. Iterative Knowledge-Based Scoring Functions Derived from Rigid and Flexible Decoy Structures: Evaluation with the 2013 and 2014 CSAR Benchmarks.
Yan C; Grinter SZ; Merideth BR; Ma Z; Zou X
J Chem Inf Model; 2016 Jun; 56(6):1013-21. PubMed ID: 26389744
[TBL] [Abstract][Full Text] [Related]
17. Discrete molecular dynamics distinguishes nativelike binding poses from decoys in difficult targets.
Proctor EA; Yin S; Tropsha A; Dokholyan NV
Biophys J; 2012 Jan; 102(1):144-51. PubMed ID: 22225808
[TBL] [Abstract][Full Text] [Related]
18. CSAR benchmark exercise 2011-2012: evaluation of results from docking and relative ranking of blinded congeneric series.
Damm-Ganamet KL; Smith RD; Dunbar JB; Stuckey JA; Carlson HA
J Chem Inf Model; 2013 Aug; 53(8):1853-70. PubMed ID: 23548044
[TBL] [Abstract][Full Text] [Related]
19. Application of the docking program SOL for CSAR benchmark.
Sulimov AV; Kutov DC; Oferkin IV; Katkova EV; Sulimov VB
J Chem Inf Model; 2013 Aug; 53(8):1946-56. PubMed ID: 23829357
[TBL] [Abstract][Full Text] [Related]
20. Integration of Ligand and Structure Based Approaches for CSAR-2014.
Prathipati P; Mizuguchi K
J Chem Inf Model; 2016 Jun; 56(6):974-87. PubMed ID: 26492437
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
