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Journal Abstract Search

363 related items for PubMed ID: 31506789

  • 1. Predicting binding poses and affinity ranking in D3R Grand Challenge using PL-PatchSurfer2.0.
    Shin WH, Kihara D.
    J Comput Aided Mol Des; 2019 Dec; 33(12):1083-1094. PubMed ID: 31506789
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  • 2. D3R Grand Challenge 4: ligand similarity and MM-GBSA-based pose prediction and affinity ranking for BACE-1 inhibitors.
    Sasmal S, El Khoury L, Mobley DL.
    J Comput Aided Mol Des; 2020 Feb; 34(2):163-177. PubMed ID: 31781990
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  • 3. Deep neural network affinity model for BACE inhibitors in D3R Grand Challenge 4.
    Wang B, Ng HL.
    J Comput Aided Mol Des; 2020 Feb; 34(2):201-217. PubMed ID: 31916049
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  • 4. 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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  • 9. D3R grand challenge 4: blind prediction of protein-ligand poses, affinity rankings, and relative binding free energies.
    Parks CD, Gaieb Z, Chiu M, Yang H, Shao C, Walters WP, Jansen JM, McGaughey G, Lewis RA, Bembenek SD, Ameriks MK, Mirzadegan T, Burley SK, Amaro RE, Gilson MK.
    J Comput Aided Mol Des; 2020 Feb; 34(2):99-119. PubMed ID: 31974851
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  • 10. 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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  • 11. 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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  • 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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  • 15. 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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  • 16. 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
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  • 17. Sampling and refinement protocols for template-based macrocycle docking: 2018 D3R Grand Challenge 4.
    Kotelnikov S, Alekseenko A, Liu C, Ignatov M, Padhorny D, Brini E, Lukin M, Coutsias E, Dill KA, Kozakov D.
    J Comput Aided Mol Des; 2020 Feb; 34(2):179-189. PubMed ID: 31879831
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  • 18. 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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  • 19. PL-PatchSurfer2: Improved Local Surface Matching-Based Virtual Screening Method That Is Tolerant to Target and Ligand Structure Variation.
    Shin WH, Christoffer CW, Wang J, Kihara D.
    J Chem Inf Model; 2016 Sep 26; 56(9):1676-91. PubMed ID: 27500657
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  • 20. 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 26; 33(12):1031-1043. PubMed ID: 31677003
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