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Journal Abstract Search

938 related items for PubMed ID: 27562018

  • 1. Large scale free energy calculations for blind predictions of protein-ligand binding: the D3R Grand Challenge 2015.
    Deng N, Flynn WF, Xia J, Vijayan RS, Zhang B, He P, Mentes A, Gallicchio E, Levy RM.
    J Comput Aided Mol Des; 2016 Sep; 30(9):743-751. PubMed ID: 27562018
    [Abstract] [Full Text] [Related]

  • 2. Binding-affinity predictions of HSP90 in the D3R Grand Challenge 2015 with docking, MM/GBSA, QM/MM, and free-energy simulations.
    Misini Ignjatović M, Caldararu O, Dong G, Muñoz-Gutierrez C, Adasme-Carreño F, Ryde U.
    J Comput Aided Mol Des; 2016 Sep; 30(9):707-730. PubMed ID: 27565797
    [Abstract] [Full Text] [Related]

  • 3. Blinded predictions of binding modes and energies of HSP90-α ligands for the 2015 D3R grand challenge.
    Mey ASJS, Juárez-Jiménez J, Hennessy A, Michel J.
    Bioorg Med Chem; 2016 Oct 15; 24(20):4890-4899. PubMed ID: 27485604
    [Abstract] [Full Text] [Related]

  • 4. 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 15; 30(9):791-804. PubMed ID: 27718029
    [Abstract] [Full Text] [Related]

  • 5. 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 15; 30(9):651-668. PubMed ID: 27696240
    [Abstract] [Full Text] [Related]

  • 6. 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 15; 32(1):21-44. PubMed ID: 29119352
    [Abstract] [Full Text] [Related]

  • 7. Interaction with specific HSP90 residues as a scoring function: validation in the D3R Grand Challenge 2015.
    Santos-Martins D.
    J Comput Aided Mol Des; 2016 Sep 15; 30(9):731-742. PubMed ID: 27549813
    [Abstract] [Full Text] [Related]

  • 8. 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 15; 33(1):105-117. PubMed ID: 30218199
    [Abstract] [Full Text] [Related]

  • 9. Improving binding mode and binding affinity predictions of docking by ligand-based search of protein conformations: evaluation in D3R grand challenge 2015.
    Xu X, Yan C, Zou X.
    J Comput Aided Mol Des; 2017 Aug 15; 31(8):689-699. PubMed ID: 28668990
    [Abstract] [Full Text] [Related]

  • 10. 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 15; 30(9):685-693. PubMed ID: 27484214
    [Abstract] [Full Text] [Related]

  • 11. Molecular docking performance evaluated on the D3R Grand Challenge 2015 drug-like ligand datasets.
    Selwa E, Martiny VY, Iorga BI.
    J Comput Aided Mol Des; 2016 Sep 15; 30(9):829-839. PubMed ID: 27699554
    [Abstract] [Full Text] [Related]

  • 12. 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 15; 32(1):75-87. PubMed ID: 28766097
    [Abstract] [Full Text] [Related]

  • 13. Docking-undocking combination applied to the D3R Grand Challenge 2015.
    Ruiz-Carmona S, Barril X.
    J Comput Aided Mol Des; 2016 Sep 15; 30(9):805-815. PubMed ID: 27709317
    [Abstract] [Full Text] [Related]

  • 14. Improved pose and affinity predictions using different protocols tailored on the basis of data availability.
    Prathipati P, Nagao C, Ahmad S, Mizuguchi K.
    J Comput Aided Mol Des; 2016 Sep 15; 30(9):817-828. PubMed ID: 27714493
    [Abstract] [Full Text] [Related]

  • 15. Optimal strategies for virtual screening of induced-fit and flexible target in the 2015 D3R Grand Challenge.
    Ye Z, Baumgartner MP, Wingert BM, Camacho CJ.
    J Comput Aided Mol Des; 2016 Sep 15; 30(9):695-706. PubMed ID: 27573981
    [Abstract] [Full Text] [Related]

  • 16. Distinguishing binders from false positives by free energy calculations: fragment screening against the flap site of HIV protease.
    Deng N, Forli S, He P, Perryman A, Wickstrom L, Vijayan RS, Tiefenbrunn T, Stout D, Gallicchio E, Olson AJ, Levy RM.
    J Phys Chem B; 2015 Jan 22; 119(3):976-88. PubMed ID: 25189630
    [Abstract] [Full Text] [Related]

  • 17. Improving Prediction Accuracy of Binding Free Energies and Poses of HIV Integrase Complexes Using the Binding Energy Distribution Analysis Method with Flattening Potentials.
    Xia J, Flynn W, Levy RM.
    J Chem Inf Model; 2018 Jul 23; 58(7):1356-1371. PubMed ID: 29927237
    [Abstract] [Full Text] [Related]

  • 18. 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 23; 33(1):93-103. PubMed ID: 30206740
    [Abstract] [Full Text] [Related]

  • 19. Shape similarity guided pose prediction: lessons from D3R Grand Challenge 3.
    Kumar A, Zhang KYJ.
    J Comput Aided Mol Des; 2019 Jan 23; 33(1):47-59. PubMed ID: 30084081
    [Abstract] [Full Text] [Related]

  • 20. 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 23; 32(1):265-272. PubMed ID: 28900792
    [Abstract] [Full Text] [Related]

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