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Journal Abstract Search

243 related items for PubMed ID: 27485604

  • 1. 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]

  • 2. 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 15; 30(9):743-751. PubMed ID: 27562018
    [Abstract] [Full Text] [Related]

  • 3. 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 15; 30(9):707-730. PubMed ID: 27565797
    [Abstract] [Full Text] [Related]

  • 4. 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]

  • 5. 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]

  • 6. 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]

  • 7. Impact of domain knowledge on blinded predictions of binding energies by alchemical free energy calculations.
    Mey ASJS, Jiménez JJ, Michel J.
    J Comput Aided Mol Des; 2018 Jan 15; 32(1):199-210. PubMed ID: 29134431
    [Abstract] [Full Text] [Related]

  • 8. 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]

  • 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. 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 15; 32(1):231-238. PubMed ID: 28913743
    [Abstract] [Full Text] [Related]

  • 11. 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]

  • 12. 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]

  • 13. 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]

  • 14. 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]

  • 15. Shape similarity guided pose prediction: lessons from D3R Grand Challenge 3.
    Kumar A, Zhang KYJ.
    J Comput Aided Mol Des; 2019 Jan 15; 33(1):47-59. PubMed ID: 30084081
    [Abstract] [Full Text] [Related]

  • 16. Performance evaluation of molecular docking and free energy calculations protocols using the D3R Grand Challenge 4 dataset.
    Elisée E, Gapsys V, Mele N, Chaput L, Selwa E, de Groot BL, Iorga BI.
    J Comput Aided Mol Des; 2019 Dec 15; 33(12):1031-1043. PubMed ID: 31677003
    [Abstract] [Full Text] [Related]

  • 17. 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]

  • 18. 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]

  • 19. Docking pose selection by interaction pattern graph similarity: application to the D3R grand challenge 2015.
    Slynko I, Da Silva F, Bret G, Rognan D.
    J Comput Aided Mol Des; 2016 Sep 15; 30(9):669-683. PubMed ID: 27480696
    [Abstract] [Full Text] [Related]

  • 20. 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 15; 32(1):273-286. PubMed ID: 28865056
    [Abstract] [Full Text] [Related]

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