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Journal Abstract Search
241 related items for PubMed ID: 30206740
1. 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]
2. 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]
3. 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; 33(12):1031-1043. PubMed ID: 31677003 [Abstract] [Full Text] [Related]
4. 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]
5. 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]
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 [Abstract] [Full Text] [Related]
7. Blinded evaluation of farnesoid X receptor (FXR) ligands binding using molecular docking and free energy calculations. Selwa E, Elisée E, Zavala A, Iorga BI. J Comput Aided Mol Des; 2018 Jan; 32(1):273-286. PubMed ID: 28865056 [Abstract] [Full Text] [Related]
8. 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]
9. Large scale free energy calculations for blind predictions of protein-ligand binding: the D3R Grand Challenge 2015. Deng N, Flynn WF, Xia J, Vijayan RS, Zhang B, He P, Mentes A, Gallicchio E, Levy RM. J Comput Aided Mol Des; 2016 Sep; 30(9):743-751. PubMed ID: 27562018 [Abstract] [Full Text] [Related]
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 [Abstract] [Full Text] [Related]
12. 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]
13. 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]
15. 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]
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 [Abstract] [Full Text] [Related]
17. Workflows and performances in the ranking prediction of 2016 D3R Grand Challenge 2: lessons learned from a collaborative effort. Gao YD, Hu Y, Crespo A, Wang D, Armacost KA, Fells JI, Fradera X, Wang H, Wang H, Sherborne B, Verras A, Peng Z. J Comput Aided Mol Des; 2018 Jan; 32(1):129-142. PubMed ID: 28986733 [Abstract] [Full Text] [Related]
18. D3R grand challenge 2015: Evaluation of protein-ligand pose and affinity predictions. Gathiaka S, Liu S, Chiu M, Yang H, Stuckey JA, Kang YN, Delproposto J, Kubish G, Dunbar JB, Carlson HA, Burley SK, Walters WP, Amaro RE, Feher VA, Gilson MK. J Comput Aided Mol Des; 2016 Sep; 30(9):651-668. PubMed ID: 27696240 [Abstract] [Full Text] [Related]
19. 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]
20. 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] Page: [Next] [New Search]