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Journal Abstract Search

149 related items for PubMed ID: 30128928

  • 1. 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]

  • 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. 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]

  • 4. 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]

  • 5. 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]

  • 6. 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]

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

  • 8. 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]

  • 9. 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]

  • 10. 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]

  • 11. 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]

  • 12. 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]

  • 13. 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; 30(9):817-828. PubMed ID: 27714493
    [Abstract] [Full Text] [Related]

  • 14. 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]

  • 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. 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]

  • 17. 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]

  • 18. 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; 30(9):669-683. PubMed ID: 27480696
    [Abstract] [Full Text] [Related]

  • 19. 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]

  • 20. 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]

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