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PUBMED FOR HANDHELDS

Journal Abstract Search


282 related items for PubMed ID: 30116918

  • 1. 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
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  • 2. 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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  • 3. 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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  • 4. MathDL: mathematical deep learning for D3R Grand Challenge 4.
    Nguyen DD, Gao K, Wang M, Wei GW.
    J Comput Aided Mol Des; 2020 Feb; 34(2):131-147. PubMed ID: 31734815
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  • 5. 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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  • 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; 32(1):21-44. PubMed ID: 29119352
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  • 7. 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
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  • 9. 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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  • 10. 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
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  • 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
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  • 15. 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
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  • 16. 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; 32(1):199-210. PubMed ID: 29134431
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  • 18. 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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  • 20. 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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