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Journal Abstract Search

488 related items for PubMed ID: 30084081

  • 1.
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  • 2. 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]

  • 3. 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
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  • 5. 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]

  • 6. 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
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  • 9. 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]

  • 10. 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]

  • 11. 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
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  • 12. 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
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  • 13. Predicting protein-ligand binding modes for CELPP and GC3: workflows and insight.
    Xu X, Ma Z, Duan R, Zou X.
    J Comput Aided Mol Des; 2019 Mar; 33(3):367-374. PubMed ID: 30689079
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  • 14. 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
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  • 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
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  • 19. 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
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