157 related articles for article (PubMed ID: 29101520)
1. Protein-ligand docking using FFT based sampling: D3R case study.
Padhorny D; Hall DR; Mirzaei H; Mamonov AB; Moghadasi M; Alekseenko A; Beglov D; Kozakov D
J Comput Aided Mol Des; 2018 Jan; 32(1):225-230. PubMed ID: 29101520
[TBL] [Abstract][Full Text] [Related]
2. Shape similarity guided pose prediction: lessons from D3R Grand Challenge 3.
Kumar A; Zhang KYJ
J Comput Aided Mol Des; 2019 Jan; 33(1):47-59. PubMed ID: 30084081
[TBL] [Abstract][Full Text] [Related]
3. Monte Carlo on the manifold and MD refinement for binding pose prediction of protein-ligand complexes: 2017 D3R Grand Challenge.
Ignatov M; Liu C; Alekseenko A; Sun Z; Padhorny D; Kotelnikov S; Kazennov A; Grebenkin I; Kholodov Y; Kolosvari I; Perez A; Dill K; Kozakov D
J Comput Aided Mol Des; 2019 Jan; 33(1):119-127. PubMed ID: 30421350
[TBL] [Abstract][Full Text] [Related]
4. Sampling and refinement protocols for template-based macrocycle docking: 2018 D3R Grand Challenge 4.
Kotelnikov S; Alekseenko A; Liu C; Ignatov M; Padhorny D; Brini E; Lukin M; Coutsias E; Dill KA; Kozakov D
J Comput Aided Mol Des; 2020 Feb; 34(2):179-189. PubMed ID: 31879831
[TBL] [Abstract][Full Text] [Related]
5. Improving ligand 3D shape similarity-based pose prediction with a continuum solvent model.
Kumar A; Zhang KYJ
J Comput Aided Mol Des; 2019 Dec; 33(12):1045-1055. PubMed ID: 31463704
[TBL] [Abstract][Full Text] [Related]
6. 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]
7. Focused grid-based resampling for protein docking and mapping.
Mamonov AB; Moghadasi M; Mirzaei H; Zarbafian S; Grove LE; Bohnuud T; Vakili P; Ch Paschalidis I; Vajda S; Kozakov D
J Comput Chem; 2016 Apr; 37(11):961-70. PubMed ID: 26837000
[TBL] [Abstract][Full Text] [Related]
8. 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]
9. Accelerating and focusing protein-protein docking correlations using multi-dimensional rotational FFT generating functions.
Ritchie DW; Kozakov D; Vajda S
Bioinformatics; 2008 Sep; 24(17):1865-73. PubMed ID: 18591193
[TBL] [Abstract][Full Text] [Related]
10. Alchemical Grid Dock (AlGDock) calculations in the D3R Grand Challenge 3 : Binding free energies between flexible ligands and rigid receptors.
Xie B; Minh DDL
J Comput Aided Mol Des; 2019 Jan; 33(1):61-69. PubMed ID: 30084078
[TBL] [Abstract][Full Text] [Related]
11. Performance of multiple docking and refinement methods in the pose prediction D3R prospective Grand Challenge 2016.
Fradera X; Verras A; Hu Y; Wang D; Wang H; Fells JI; Armacost KA; Crespo A; Sherborne B; Wang H; Peng Z; Gao YD
J Comput Aided Mol Des; 2018 Jan; 32(1):113-127. PubMed ID: 28913710
[TBL] [Abstract][Full Text] [Related]
12. Exploring protein-protein interactions using the site-identification by ligand competitive saturation methodology.
Yu W; Jo S; Lakkaraju SK; Weber DJ; MacKerell AD
Proteins; 2019 Apr; 87(4):289-301. PubMed ID: 30582220
[TBL] [Abstract][Full Text] [Related]
13. Ranking docking poses by graph matching of protein-ligand interactions: lessons learned from the D3R Grand Challenge 2.
da Silva Figueiredo Celestino Gomes P; Da Silva F; Bret G; Rognan D
J Comput Aided Mol Des; 2018 Jan; 32(1):75-87. PubMed ID: 28766097
[TBL] [Abstract][Full Text] [Related]
14. Accelerated CDOCKER with GPUs, Parallel Simulated Annealing, and Fast Fourier Transforms.
Ding X; Wu Y; Wang Y; Vilseck JZ; Brooks CL
J Chem Theory Comput; 2020 Jun; 16(6):3910-3919. PubMed ID: 32374996
[TBL] [Abstract][Full Text] [Related]
15. Docking of small molecules to farnesoid X receptors using AutoDock Vina with the Convex-PL potential: lessons learned from D3R Grand Challenge 2.
Kadukova M; Grudinin S
J Comput Aided Mol Des; 2018 Jan; 32(1):151-162. PubMed ID: 28913782
[TBL] [Abstract][Full Text] [Related]
16. Ligand-biased ensemble receptor docking (LigBEnD): a hybrid ligand/receptor structure-based approach.
Lam PC; Abagyan R; Totrov M
J Comput Aided Mol Des; 2018 Jan; 32(1):187-198. PubMed ID: 28887659
[TBL] [Abstract][Full Text] [Related]
17. Hybrid receptor structure/ligand-based docking and activity prediction in ICM: development and evaluation in D3R Grand Challenge 3.
Lam PC; Abagyan R; Totrov M
J Comput Aided Mol Des; 2019 Jan; 33(1):35-46. PubMed ID: 30094533
[TBL] [Abstract][Full Text] [Related]
18. Macrocycle modeling in ICM: benchmarking and evaluation in D3R Grand Challenge 4.
Lam PC; Abagyan R; Totrov M
J Comput Aided Mol Des; 2019 Dec; 33(12):1057-1069. PubMed ID: 31598897
[TBL] [Abstract][Full Text] [Related]
19. Combining self- and cross-docking as benchmark tools: the performance of DockBench in the D3R Grand Challenge 2.
Salmaso V; Sturlese M; Cuzzolin A; Moro S
J Comput Aided Mol Des; 2018 Jan; 32(1):251-264. PubMed ID: 28840418
[TBL] [Abstract][Full Text] [Related]
20. Prospective evaluation of shape similarity based pose prediction method in D3R Grand Challenge 2015.
Kumar A; Zhang KY
J Comput Aided Mol Des; 2016 Sep; 30(9):685-693. PubMed ID: 27484214
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
