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Journal Abstract Search

457 related items for PubMed ID: 28913782

  • 1. 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
    [Abstract] [Full Text] [Related]

  • 2. Predicting the affinity of Farnesoid X Receptor ligands through a hierarchical ranking protocol: a D3R Grand Challenge 2 case study.
    Réau M, Langenfeld F, Zagury JF, Montes M.
    J Comput Aided Mol Des; 2018 Jan; 32(1):231-238. PubMed ID: 28913743
    [Abstract] [Full Text] [Related]

  • 3. Blinded evaluation of farnesoid X receptor (FXR) ligands binding using molecular docking and free energy calculations.
    Selwa E, Elisée E, Zavala A, Iorga BI.
    J Comput Aided Mol Des; 2018 Jan; 32(1):273-286. PubMed ID: 28865056
    [Abstract] [Full Text] [Related]

  • 4. 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
    [Abstract] [Full Text] [Related]

  • 5. 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
    [Abstract] [Full Text] [Related]

  • 6. 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
    [Abstract] [Full Text] [Related]

  • 7. 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
    [Abstract] [Full Text] [Related]

  • 8. 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
    [Abstract] [Full Text] [Related]

  • 9. Binding pose and affinity prediction in the 2016 D3R Grand Challenge 2 using the Wilma-SIE method.
    Hogues H, Sulea T, Gaudreault F, Corbeil CR, Purisima EO.
    J Comput Aided Mol Des; 2018 Jan; 32(1):143-150. PubMed ID: 28983727
    [Abstract] [Full Text] [Related]

  • 10. CDOCKER and λ-dynamics for prospective prediction in D₃R Grand Challenge 2.
    Ding X, Hayes RL, Vilseck JZ, Charles MK, Brooks CL.
    J Comput Aided Mol Des; 2018 Jan; 32(1):89-102. PubMed ID: 28884249
    [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
    [Abstract] [Full Text] [Related]

  • 12. Lessons learned from participating in D3R 2016 Grand Challenge 2: compounds targeting the farnesoid X receptor.
    Duan R, Xu X, Zou X.
    J Comput Aided Mol Des; 2018 Jan; 32(1):103-111. PubMed ID: 29127582
    [Abstract] [Full Text] [Related]

  • 13. Relative binding affinity prediction of farnesoid X receptor in the D3R Grand Challenge 2 using FEP.
    Schindler C, Rippmann F, Kuhn D.
    J Comput Aided Mol Des; 2018 Jan; 32(1):265-272. PubMed ID: 28900792
    [Abstract] [Full Text] [Related]

  • 14. Lessons learned in induced fit docking and metadynamics in the Drug Design Data Resource Grand Challenge 2.
    Baumgartner MP, Evans DA.
    J Comput Aided Mol Des; 2018 Jan; 32(1):45-58. PubMed ID: 29127581
    [Abstract] [Full Text] [Related]

  • 15. Performance of HADDOCK and a simple contact-based protein-ligand binding affinity predictor in the D3R Grand Challenge 2.
    Kurkcuoglu Z, Koukos PI, Citro N, Trellet ME, Rodrigues JPGLM, Moreira IS, Roel-Touris J, Melquiond ASJ, Geng C, Schaarschmidt J, Xue LC, Vangone A, Bonvin AMJJ.
    J Comput Aided Mol Des; 2018 Jan; 32(1):175-185. PubMed ID: 28831657
    [Abstract] [Full Text] [Related]

  • 16. 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
    [Abstract] [Full Text] [Related]

  • 17. 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
    [Abstract] [Full Text] [Related]

  • 18. D3R Grand Challenge 4: ligand similarity and MM-GBSA-based pose prediction and affinity ranking for BACE-1 inhibitors.
    Sasmal S, El Khoury L, Mobley DL.
    J Comput Aided Mol Des; 2020 Feb; 34(2):163-177. PubMed ID: 31781990
    [Abstract] [Full Text] [Related]

  • 19. Calculate protein-ligand binding affinities with the extended linear interaction energy method: application on the Cathepsin S set in the D3R Grand Challenge 3.
    He X, Man VH, Ji B, Xie XQ, Wang J.
    J Comput Aided Mol Des; 2019 Jan; 33(1):105-117. PubMed ID: 30218199
    [Abstract] [Full Text] [Related]

  • 20. Workflows and performances in the ranking prediction of 2016 D3R Grand Challenge 2: lessons learned from a collaborative effort.
    Gao YD, Hu Y, Crespo A, Wang D, Armacost KA, Fells JI, Fradera X, Wang H, Wang H, Sherborne B, Verras A, Peng Z.
    J Comput Aided Mol Des; 2018 Jan; 32(1):129-142. PubMed ID: 28986733
    [Abstract] [Full Text] [Related]

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