These tools will no longer be maintained as of December 31, 2024. Archived website can be found here. PubMed4Hh GitHub repository can be found here. Contact NLM Customer Service if you have questions.
8. 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]
9. Predicting binding poses and affinities for protein - ligand complexes in the 2015 D3R Grand Challenge using a physical model with a statistical parameter estimation. Grudinin S; Kadukova M; Eisenbarth A; Marillet S; Cazals F J Comput Aided Mol Des; 2016 Sep; 30(9):791-804. PubMed ID: 27718029 [TBL] [Abstract][Full Text] [Related]
10. Docking rigid macrocycles using Convex-PL, AutoDock Vina, and RDKit in the D3R Grand Challenge 4. Kadukova M; Chupin V; Grudinin S J Comput Aided Mol Des; 2020 Feb; 34(2):191-200. PubMed ID: 31784861 [TBL] [Abstract][Full Text] [Related]
11. Using physics-based pose predictions and free energy perturbation calculations to predict binding poses and relative binding affinities for FXR ligands in the D3R Grand Challenge 2. Athanasiou C; Vasilakaki S; Dellis D; Cournia Z J Comput Aided Mol Des; 2018 Jan; 32(1):21-44. PubMed ID: 29119352 [TBL] [Abstract][Full Text] [Related]
12. Convex-PL: a novel knowledge-based potential for protein-ligand interactions deduced from structural databases using convex optimization. Kadukova M; Grudinin S J Comput Aided Mol Des; 2017 Oct; 31(10):943-958. PubMed ID: 28921375 [TBL] [Abstract][Full Text] [Related]
13. Predicting binding poses and affinity ranking in D3R Grand Challenge using PL-PatchSurfer2.0. Shin WH; Kihara D J Comput Aided Mol Des; 2019 Dec; 33(12):1083-1094. PubMed ID: 31506789 [TBL] [Abstract][Full Text] [Related]
14. Comprehensive evaluation of ten docking programs on a diverse set of protein-ligand complexes: the prediction accuracy of sampling power and scoring power. Wang Z; Sun H; Yao X; Li D; Xu L; Li Y; Tian S; Hou T Phys Chem Chem Phys; 2016 May; 18(18):12964-75. PubMed ID: 27108770 [TBL] [Abstract][Full Text] [Related]
15. GSScore: a novel Graphormer-based shell-like scoring method for protein-ligand docking. Guo L; Wang J Brief Bioinform; 2024 Mar; 25(3):. PubMed ID: 38706316 [TBL] [Abstract][Full Text] [Related]
16. 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; 53(8):1915-22. PubMed ID: 23809015 [TBL] [Abstract][Full Text] [Related]
17. 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]
18. HybridDock: A Hybrid Protein-Ligand Docking Protocol Integrating Protein- and Ligand-Based Approaches. Huang SY; Li M; Wang J; Pan Y J Chem Inf Model; 2016 Jun; 56(6):1078-87. PubMed ID: 26317502 [TBL] [Abstract][Full Text] [Related]
19. 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]