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Journal Abstract Search

218 related items for PubMed ID: 30632055

  • 1. D3R Grand Challenge 3: blind prediction of protein-ligand poses and affinity rankings.
    Gaieb Z, Parks CD, Chiu M, Yang H, Shao C, Walters WP, Lambert MH, Nevins N, Bembenek SD, Ameriks MK, Mirzadegan T, Burley SK, Amaro RE, Gilson MK.
    J Comput Aided Mol Des; 2019 Jan; 33(1):1-18. PubMed ID: 30632055
    [Abstract] [Full Text] [Related]

  • 2. Mathematical deep learning for pose and binding affinity prediction and ranking in D3R Grand Challenges.
    Nguyen DD, Cang Z, Wu K, Wang M, Cao Y, Wei GW.
    J Comput Aided Mol Des; 2019 Jan; 33(1):71-82. PubMed ID: 30116918
    [Abstract] [Full Text] [Related]

  • 3. Protein-ligand pose and affinity prediction: Lessons from D3R Grand Challenge 3.
    Koukos PI, Xue LC, Bonvin AMJJ.
    J Comput Aided Mol Des; 2019 Jan; 33(1):83-91. PubMed ID: 30128928
    [Abstract] [Full Text] [Related]

  • 4. D3R Grand Challenge 2: blind prediction of protein-ligand poses, affinity rankings, and relative binding free energies.
    Gaieb Z, Liu S, Gathiaka S, Chiu M, Yang H, Shao C, Feher VA, Walters WP, Kuhn B, Rudolph MG, Burley SK, Gilson MK, Amaro RE.
    J Comput Aided Mol Des; 2018 Jan; 32(1):1-20. PubMed ID: 29204945
    [Abstract] [Full Text] [Related]

  • 5. 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]

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

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

  • 8. D3R grand challenge 4: blind prediction of protein-ligand poses, affinity rankings, and relative binding free energies.
    Parks CD, Gaieb Z, Chiu M, Yang H, Shao C, Walters WP, Jansen JM, McGaughey G, Lewis RA, Bembenek SD, Ameriks MK, Mirzadegan T, Burley SK, Amaro RE, Gilson MK.
    J Comput Aided Mol Des; 2020 Feb; 34(2):99-119. PubMed ID: 31974851
    [Abstract] [Full Text] [Related]

  • 9. Blinded evaluation of cathepsin S inhibitors from the D3RGC3 dataset using molecular docking and free energy calculations.
    Chaput L, Selwa E, Elisée E, Iorga BI.
    J Comput Aided Mol Des; 2019 Jan; 33(1):93-103. PubMed ID: 30206740
    [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
    [Abstract] [Full Text] [Related]

  • 11. 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]

  • 12. 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
    [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
    [Abstract] [Full Text] [Related]

  • 14. 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
    [Abstract] [Full Text] [Related]

  • 15. Exploring fragment-based target-specific ranking protocol with machine learning on cathepsin S.
    Yang Y, Lu J, Yang C, Zhang Y.
    J Comput Aided Mol Des; 2019 Dec; 33(12):1095-1105. PubMed ID: 31729618
    [Abstract] [Full Text] [Related]

  • 16. 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]

  • 17. D3R Grand Challenge 4: prospective pose prediction of BACE1 ligands with AutoDock-GPU.
    Santos-Martins D, Eberhardt J, Bianco G, Solis-Vasquez L, Ambrosio FA, Koch A, Forli S.
    J Comput Aided Mol Des; 2019 Dec; 33(12):1071-1081. PubMed ID: 31691920
    [Abstract] [Full Text] [Related]

  • 18. 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]

  • 19. D3R grand challenge 2015: Evaluation of protein-ligand pose and affinity predictions.
    Gathiaka S, Liu S, Chiu M, Yang H, Stuckey JA, Kang YN, Delproposto J, Kubish G, Dunbar JB, Carlson HA, Burley SK, Walters WP, Amaro RE, Feher VA, Gilson MK.
    J Comput Aided Mol Des; 2016 Sep; 30(9):651-668. PubMed ID: 27696240
    [Abstract] [Full Text] [Related]

  • 20. 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]

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