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Journal Abstract Search

169 related items for PubMed ID: 30094533

  • 1. 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]

  • 2. 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
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  • 4. 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]

  • 5. 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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  • 7. 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]

  • 8. Ligand-biased ensemble receptor docking (LigBEnD): a hybrid ligand/receptor structure-based approach.
    Lam PC, Abagyan R, Totrov M.
    J Comput Aided Mol Des; 2018 Jan; 32(1):187-198. PubMed ID: 28887659
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  • 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. 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
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  • 12. 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
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  • 13. Lessons learned from participating in D3R 2016 Grand Challenge 2: compounds targeting the farnesoid X receptor.
    Duan R, Xu X, Zou X.
    J Comput Aided Mol Des; 2018 Jan; 32(1):103-111. PubMed ID: 29127582
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  • 14. 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
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  • 15. 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]

  • 16. 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]

  • 17. Combining self- and cross-docking as benchmark tools: the performance of DockBench in the D3R Grand Challenge 2.
    Salmaso V, Sturlese M, Cuzzolin A, Moro S.
    J Comput Aided Mol Des; 2018 Jan; 32(1):251-264. PubMed ID: 28840418
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  • 18. 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]

  • 19. CDOCKER and λ-dynamics for prospective prediction in D₃R Grand Challenge 2.
    Ding X, Hayes RL, Vilseck JZ, Charles MK, Brooks CL.
    J Comput Aided Mol Des; 2018 Jan; 32(1):89-102. PubMed ID: 28884249
    [Abstract] [Full Text] [Related]

  • 20. 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]

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