Latest developments in molecular docking: 2010-2011 in review

Journal of Molecular Recognition

Volumn 26, Issue 5, 2013, Pages 215-239

  Yuriev, Elizabeth ^a   Ramsland, Paul A ^b,c,d  

^a MONASH UNIVERSITY   (Australia)

^b BURNET INSTITUTE   (Australia)

^c UNIVERSITY OF MELBOURNE   (Australia)

^d MONASH UNIVERSITY   (Australia)

Author keywords

binding mode prediction; GPCR; kinase; postprocessing; receptor flexibility; scoring; validation; virtual screening

Indexed keywords

LIGAND;

ALGORITHM; BINDING SITE; CHEMICAL STRUCTURE; DRUG DESIGN; MOLECULAR DOCKING; MONTE CARLO METHOD; PROTEIN BINDING; REVIEW;

ALGORITHMS; BINDING SITES; DRUG DESIGN; LIGANDS; MODELS, MOLECULAR; MOLECULAR DOCKING SIMULATION; MONTE CARLO METHOD; PROTEIN BINDING;

EID: 84875480305     PISSN: 09523499     EISSN: 10991352     Source Type: Journal    
DOI: 10.1002/jmr.2266     Document Type: Review

References (218)

1. Waszkowycz B, Clark DE, Gancia E,. 2011. Outstanding challenges in protein-ligand docking and structure-based virtual screening. WIREs Comput. Mol. Sci. 1: 229-259.
2. Bourquard T, Bernauer J, Aze J, Poupon A,. 2011. A collaborative filtering approach for protein-protein docking scoring functions. PLoS One 6: e18541.
3. Hernandes MZ, de SPFJ, Coelho LC, Moreira DR, Pereira VR, Leite AC,. 2010. Recent insights on the medicinal chemistry of metal-based compounds: hints for the successful drug design. Curr. Med. Chem. 17: 3739-3750.
4. Yuriev E, Agostino M, Ramsland PA,. 2011. Challenges and advances in computational docking: 2009 in review. J. Mol. Recognit. 24: 149-164.
5. Sadjad BS, Zsoldos Z,. 2011. Toward a robust search method for the protein-drug docking problem. IEEE/ACM Trans. Comput. Biol. Bioinform. 8: 1120-1133.
6. Takaya D, Yamashita A, Kamijo K, Gomi J, Ito M, Maekawa S, Enomoto N, Sakamoto N, Watanabe Y, Arai R, Umeyama H, Honma T, Matsumoto T, Yokoyama S,. 2011. A new method for induced fit docking (GENIUS) and its application to virtual screening of novel HCV NS3-4A protease inhibitors. Bioorg. Med. Chem. 19: 6892-6905.
7. Luo W, Pei J, Zhu Y,. 2010. A fast protein-ligand docking algorithm based on hydrogen bond matching and surface shape complementarity. J. Mol. Model. 16: 903-913.
8. Klenner A, Weisel M, Reisen F, Proschak E, Schneider G,. 2010. Automated docking of flexible molecules into receptor binding sites by ligand self-organization in situ. Mol. Inf. 29: 189-193.
9. Kim DS, Kim CM, Won CI, Kim JK, Ryu J, Cho Y, Lee C, Bhak J,. 2011. BetaDock: shape-priority docking method based on beta-complex. J. Biomol. Struct. Dyn. 29: 219-42.
10. Vorobjev YN,. 2010. Blind docking method combining search of low-resolution binding sites with ligand pose refinement by molecular dynamics-based global optimization. J. Comput. Chem. 31: 1080-1092.
11. Brylinski M, Skolnick J,. 2010. Q-Dock(LHM): low-resolution refinement for ligand comparative modeling. J. Comput. Chem. 31: 1093-1105.
12. Shin WH, Heo L, Lee J, Ko J, Seok C, Lee J,. 2011. LigDockCSA: protein-ligand docking using conformational space annealing. J. Comput. Chem. 32: 3226-3232.
13. Grosdidier A, Zoete V, Michielin O,. 2011. Fast docking using the CHARMM force field with EADock DSS. J. Comput. Chem. 32: 2149-2159.
14. Takahashi O, Masuda Y, Muroya A, Furuya T,. 2010. Theory of docking scores and its application to a customizable scoring function. SAR QSAR Environ. Res. 21: 547-558.
15. Feng JA, Marshall GR,. 2010. SKATE: a docking program that decouples systematic sampling from scoring. J. Comput. Chem. 31: 2540-2454.
16. Chung JY, Cho SJ, Hah JM,. 2011. A python-based docking program utilizing a receptor bound ligand shape: PythDock. Arch. Pharm. Res. 34: 1451-1458.
17. Plewczynski D, Lazniewski M, Von Grotthuss M, Rychlewski L, Ginalski K,. 2011. VoteDock: consensus docking method for prediction of protein-ligand interactions. J. Comput. Chem. 32: 568-581.
18. Ding F, Yin S, Dokholyan NV,. 2010. Rapid flexible docking using a stochastic rotamer library of ligands. J. Chem. Inf. Model. 50: 1623-1632.
19. Trott O, Olson AJ,. 2010. AutoDock Vina: improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading. J. Comput. Chem. 31: 455-461.
20. Meier R, Pippel M, Brandt F, Sippl W, Baldauf C,. 2010. ParaDockS: a framework for molecular docking with population-based metaheuristics. J. Chem. Inf. Model. 50: 879-889.
21. Lill MA,. 2011. Efficient incorporation of protein flexibility and dynamics into molecular docking simulations. Biochemistry 50: 6157-669.
22. Sotriffer CA,. 2011. Accounting for induced-fit effects in docking: what is possible and what is not? Curr. Top. Med. Chem. 11: 179-191.
23. Borrelli KW, Cossins B, Guallar V,. 2010. Exploring hierarchical refinement techniques for induced fit docking with protein and ligand flexibility. J. Comput. Chem. 31: 1224-1235.
24. Meng EC, Shoichet BK, Kuntz ID,. 1992. Automated docking with grid-based energy evaluation. J. Comput. Chem. 13: 505-524.
25. Craig IR, Essex JW, Spiegel K,. 2010. Ensemble docking into multiple crystallographically derived protein structures: an evaluation based on the statistical analysis of enrichments. J. Chem. Inf. Model. 50: 511-524.
26. Friesner RA, Banks JL, Murphy RB, Halgren TA, Klicic JJ, Mainz DT, Repasky MP, Knoll EH, Shelley M, Perry JK, Shaw DE, Francis P, Shenkin PS,. 2004. Glide: a new approach for rapid, accurate docking and scoring. 1. Method and assessment of docking accuracy. J. Med. Chem. 47: 1739-1749.
27. Rueda M, Bottegoni G, Abagyan R,. 2010. Recipes for the selection of experimental protein conformations for virtual screening. J. Chem. Inf. Model. 50: 186-193.
28. Abagyan RA, Totrov MM, Kuznetsov DA,. 1994. ICM: a new method for protein modeling and design: Applications to docking and structure prediction from the distorted native conformation. J. Comput. Chem. 15: 488-506.
29. Brooijmans N, Humblet C,. 2010. Chemical space sampling in virtual screening by different crystal structures. Chem. Biol. Drug Des. 76: 472-479.
30. Bottegoni G, Rocchia W, Rueda M, Abagyan R, Cavalli A,. 2011. Systematic exploitation of multiple receptor conformations for virtual ligand screening. PLoS One 6: e18845.
31. Verdonk ML, Cole JC, Hartshorn MJ, Murray CW, Taylor RD,. 2003. Improved protein-ligand docking using GOLD. Proteins 52: 609-623.
32. Haider MK, Bertrand HO, Hubbard RE,. 2011. Predicting fragment binding poses using a combined MCSS MM-GBSA approach. J. Chem. Inf. Model. 51: 1092-1105.
33. Li Y, Liu Z, Wang R,. 2010. Test MM-PB/SA on true conformational ensembles of protein-ligand complexes. J. Chem. Inf. Model. 50: 1682-1692.
34. Korb O, McCabe P, Cole J,. 2011. The ensemble performance index: an improved measure for assessing ensemble pose prediction performance. J. Chem. Inf. Model. 51: 2915-2919.
35. Brooijmans N, Humblet C,. 2010. Chemical space sampling by different scoring functions and crystal structures. J. Comput. Aided Mol. Des. 24: 433-447.
36. Li Y, Kim DJ, Ma W, Lubet RA, Bode AM, Dong Z,. 2011. Discovery of novel checkpoint kinase 1 inhibitors by virtual screening based on multiple crystal structures. J. Chem. Inf. Model. 51: 2904-2914.
37. Park SJ, Kufareva I, Abagyan R,. 2010. Improved docking, screening and selectivity prediction for small molecule nuclear receptor modulators using conformational ensembles. J. Comput. Aided Mol. Des. 24: 459-471.
38. Novoa EM, de Pouplana LR, Barril X, Orozco M,. 2010. Ensemble docking from homology models. J. Chem. Theory. Comput. 6: 2547-2557.
39. Sperandio O, Mouawad L, Pinto E, Villoutreix BO, Perahia D, Miteva MA,. 2010. How to choose relevant multiple receptor conformations for virtual screening: a test case of Cdk2 and normal mode analysis. Eur. Biophys. J. 39: 1365-1372.
40. Nichols SE, Baron R, Ivetac A, McCammon JA,. 2011. Predictive power of molecular dynamics receptor structures in virtual screening. J. Chem. Inf. Model. 51: 1439-1446.
41. Machado KS, Winck AT, Ruiz DD, de Souza ON,. 2010. Mining flexible-receptor docking experiments to select promising protein receptor snapshots. BMC Genomics 11 (5): S6.
42. Morris GM, Goodsell DS, Halliday RS, Huey R, Hart WE, Belew RK, Olson AJ,. 1998. Automated docking using a Lamarckian genetic algorithm and an empirical binding free energy function. J. Comput. Chem. 19: 1639-1662.
43. Machado KS, Schroeder EK, Ruiz DD, Cohen EM, de Souza ON,. 2011. FReDoWS: a method to automate molecular docking simulations with explicit receptor flexibility and snapshots selection. BMC Genomics 12 (4): S6.
44. Xu M, Lill MA,. 2011. Significant enhancement of docking sensitivity using implicit ligand sampling. J. Chem. Inf. Model. 51: 693-706.
45. Phatak SS, Gatica EA, Cavasotto CN,. 2010. Ligand-steered modeling and docking: a benchmarking study in class a G-protein-coupled receptors. J. Chem. Inf. Model. 50: 2119-2128.
46. Vilar S, Ferino G, Phatak SS, Berk B, Cavasotto CN, Costanzi S,. 2011. Docking-based virtual screening for ligands of G protein-coupled receptors: not only crystal structures but also in silico models. J. Mol. Graph. Model. 29: 614-623.
47. McRobb FM, Capuano B, Crosby IT, Chalmers D, Yuriev E,. 2010. Homology modeling and docking evaluation of aminergic G protein-coupled receptors. J. Chem. Inf. Model. 50: 626-637.
48. Katritch V, Kufareva I, Abagyan R,. 2011. Structure based prediction of subtype-selectivity for adenosine receptor antagonists. Neuropharmacology 60: 108-15.
49. Katritch V, Rueda M, Lam PC, Yeager M, Abagyan R,. 2010. GPCR 3D homology models for ligand screening: lessons learned from blind predictions of adenosine A2a receptor complex. Proteins 78: 197-211.
50. Seeliger D, de Groot BL,. 2010. Conformational transitions upon ligand binding: holo-structure prediction from apo conformations. PLoS Comput. Biol. 6: e1000634.
51. Whalen KL, Chang KM, Spies MA,. 2011. Hybrid steered molecular dynamics-docking: an efficient solution to the problem of ranking inhibitor affinities against a flexible drug target. Mol. Inf. 30: 459-471.
52. Antes I,. 2010. DynaDock: a new molecular dynamics-based algorithm for protein-peptide docking including receptor flexibility. Proteins 78: 1084-1104.
53. Leis S, Zacharias M,. 2011. Efficient inclusion of receptor flexibility in grid-based protein-ligand docking. J. Comput. Chem. 32: 3433-3439.
54. Feher M, Williams CI,. 2010. Reducing docking score variations arising from input differences. J. Chem. Inf. Model. 50: 1549-1560.
55. Jain AN,. 2003. Surflex: fully automatic flexible molecular docking using a molecular similarity-based search engine. J. Med. Chem. 46: 499-511.
56. Plewczynski D, Lazniewski M, Augustyniak R, Ginalski K,. 2011. Can we trust docking results? Evaluation of seven commonly used programs on PDBbind database. J. Comput. Chem. 32: 742-755.
57. Yongye AB, Bender A, Martinez-Mayorga K,. 2010. Dynamic clustering threshold reduces conformer ensemble size while maintaining a biologically relevant ensemble. J. Comput. Aided Mol. Des. 24: 675-686.
58. Fuhrmann J, Rurainski A, Lenhof HP, Neumann D,. 2010. A new Lamarckian genetic algorithm for flexible ligand-receptor docking. J. Comput. Chem. 31: 1911-1918.
59. Milletti F, Vulpetti A,. 2010. Tautomer preference in PDB complexes and its impact on structure-based drug discovery. J. Chem. Inf. Model. 50: 1062-1074.
60. Park MS, Gao C, Stern HA,. 2011. Estimating binding affinities by docking/scoring methods using variable protonation states. Proteins 79: 304-314.
61. ten Brink T, Exner TE,. 2010. pK(a) based protonation states and microspecies for protein-ligand docking. J. Comput. Aided Mol. Des. 24: 935-942.
62. vLi H, Li C,. 2010. Multiple ligand simultaneous docking: orchestrated dancing of ligands in binding sites of protein. J. Comput. Chem. 31: 2014-2022.
63. Huang SY, Zou X,. 2010. Inclusion of solvation and entropy in the knowledge-based scoring function for protein-ligand interactions. J. Chem. Inf. Model. 50: 262-273.
64. Yang T, Wu JC, Yan C, Wang Y, Luo R, Gonzales MB, Dalby KN, Ren P,. 2011. Virtual screening using molecular simulations. Proteins 79: 1940-51.
65. Thilagavathi R, Mancera RL,. 2010. Ligand-protein cross-docking with water molecules. J. Chem. Inf. Model. 50: 415-21.
66. Santos R, Hritz J, Oostenbrink C,. 2010. Role of water in molecular docking simulations of cytochrome P450 2D6. J. Chem. Inf. Model. 50: 146-54.
67. Rarey M, Kramer B, Lengauer T, Klebe G,. 1996. A fast flexible docking method using an incremental construction algorithm. J. Mol. Biol. 261: 470-89.
68. Cappel D, Wahlstrom R, Brenk R, Sotriffer CA,. 2011. Probing the dynamic nature of water molecules and their influences on ligand binding in a model binding site. J. Chem. Inf. Model. 51: 2581-94.
69. Lu SY, Jiang YJ, Lv J, Zou JW, Wu TX,. 2011. Role of bridging water molecules in GSK3 beta-inhibitor complexes: Insights from QM/MM, MD, and molecular docking studies. J. Comput. Chem. 32: 1907-1918.
70. Lie MA, Thomsen R, Pedersen CN, Schiott B, Christensen MH,. 2011. Molecular docking with ligand attached water molecules. J. Chem. Inf. Model. 51: 909-17.
71. Wang R, Lu Y, Wang S,. 2003. Comparative evaluation of 11 scoring functions for molecular docking. J. Med. Chem. 46: 2287-303.
72. Muegge I, Martin YC,. 1999. A general and fast scoring function for protein-ligand interactions: a simplified potential approach. J. Med. Chem. 42: 791-804.
73. Nunez S, Venhorst J, Kruse CG,. 2010. Assessment of a novel scoring method based on solvent accessible surface area descriptors. J. Chem. Inf. Model. 50: 480-6.
74. Hou TJ, Wang JM, Li YY, Wang W,. 2011. Assessing the performance of the Molecular Mechanics/Poisson Boltzmann Surface Area and Molecular Mechanics/Generalized Born Surface Area Methods. II. The accuracy of ranking poses generated from docking. J. Comput. Chem. 32: 866-877.
75. Mysinger MM, Shoichet BK,. 2010. Rapid context-dependent ligand desolvation in molecular docking. J. Chem. Inf. Model. 50: 1561-73.
76. Guimaraes CR, Mathiowetz AM,. 2010. Addressing limitations with the MM-GB/SA scoring procedure using the WaterMap method and free energy perturbation calculations. J. Chem. Inf. Model. 50: 547-59.
77. Wichapong K, Lawson M, Pianwanit S, Kokpol S, Sippl W,. 2010. Postprocessing of protein-ligand docking poses using linear response MM-PB/SA: application to Wee1 kinase inhibitors. J. Chem. Inf. Model. 50: 1574-88.
78. Pencheva T, Soumana OS, Pajeva I, Miteva MA,. 2010. Post-docking virtual screening of diverse binding pockets: comparative study using DOCK, AMMOS, X-Score and FRED scoring functions. Eur. J. Med. Chem. 45: 2622-8.
79. Lindstrom A, Edvinsson L, Johansson A, Andersson CD, Andersson IE, Raubacher F, Linusson A,. 2011. Postprocessing of docked protein-ligand complexes using implicit solvation models. J. Chem. Inf. Model. 51: 267-82.
80. Rapp C, Kalyanaraman C, Schiffmiller A, Schoenbrun EL, Jacobson MP,. 2011. A molecular mechanics approach to modeling protein-ligand interactions: relative binding affinities in congeneric series. J. Chem. Inf. Model. 51: 2082-9.
81. Garden DP, Zhorov BS,. 2010. Docking flexible ligands in proteins with a solvent exposure- and distance-dependent dielectric function. J. Comput. Aided Mol. Des. 24: 91-105.
82. Zoete V, Grosdidier A, Cuendet M, Michielin O,. 2010. Use of the FACTS solvation model for protein-ligand docking calculations. Application to EADock. J. Mol. Recognit. 23: 457-61.
83. Fan H, Schneidman-Duhovny D, Irwin JJ, Dong G, Shoichet BK, Sali A,. 2011. Statistical potential for modeling and ranking of protein-ligand interactions. J. Chem. Inf. Model. 51: 3078-92.
84. Xie ZR, Hwang MJ,. 2010. An interaction-motif-based scoring function for protein-ligand docking. BMC Bioinformatics 11: 298.
85. Cheng T, Liu Z, Wang R,. 2010. A knowledge-guided strategy for improving the accuracy of scoring functions in binding affinity prediction. BMC Bioinformatics 11: 193.
86. Neudert G, Klebe G,. 2011. DSX: A Knowledge-Based Scoring Function for the Assessment of Protein-Ligand Complexes. J. Chem. Inf. Model. 51: 2731-2745.
87. Zheng M, Xiong B, Luo C, Li S, Liu X, Shen Q, Li J, Zhu W, Luo X, Jiang H,. 2011. Knowledge-based scoring functions in drug design: 3. A two-dimensional knowledge-based hydrogen-bonding potential for the prediction of protein-ligand interactions. J. Chem. Inf. Model. 51: 2994-3004.
88. Teramoto R, Kashima H,. 2010. Prediction of protein-ligand binding affinities using multiple instance learning. J. Mol. Graph. Model. 29: 492-7.
89. Avram S, Pacureanu LM, Seclaman E, Bora A, Kurunczi L,. 2011. PLS-DA-Docking Optimized Combined Energetic Terms (PLSDA-DOCET) protocol: a brief evaluation. J. Chem. Inf. Model. 51: 3169-79.
90. McGann M,. 2011. FRED pose prediction and virtual screening accuracy. J. Chem. Inf. Model. 51: 578-96.
91. Hsieh JH, Yin S, Liu S, Sedykh A, Dokholyan NV, Tropsha A,. 2011. Combined application of cheminformatics- and physical force field-based scoring functions improves binding affinity prediction for CSAR data sets. J. Chem. Inf. Model. 51: 2027-35.
92. Kukol A,. 2011. Consensus virtual screening approaches to predict protein ligands. Eur. J. Med. Chem. 46: 4661-4.
93. Ewing TJ, Makino S, Skillman AG, Kuntz ID,. 2001. DOCK 4.0: search strategies for automated molecular docking of flexible molecule databases. J. Comput. Aided Mol. Des. 15: 411-28.
94. Okamoto M, Masuda Y, Muroya A, Yasuno K, Takahashi O, Furuya T,. 2010. Evaluation of docking calculations on X-ray structures using CONSENSUS-DOCK. Chem. Pharm. Bull. 58: 1655-7.
95. Plewczynski D, Philips A, von Grotthuss M, Rychlewski L, Ginalski K,. 2011. HarmonyDOCK: The structural analysis of poses in protein-ligand docking. J. Comput. Biol. 18: 1-10.
96. Durrant JD, McCammon JA,. 2010. NNScore: a neural-network-based scoring function for the characterization of protein-ligand complexes. J. Chem. Inf. Model. 50: 1865-71.
97. Durrant JD, McCammon JA,. 2011. NNScore 2.0: a neural-network receptor-ligand scoring function. J. Chem. Inf. Model. 51: 2897-903.
98. Ballester PJ, Mitchell JB,. 2010. A machine learning approach to predicting protein-ligand binding affinity with applications to molecular docking. Bioinformatics 26: 1169-75.
99. Kinnings SL, Liu N, Tonge PJ, Jackson RM, Xie L, Bourne PE,. 2011. A machine learning-based method to improve docking scoring functions and its application to drug repurposing. J. Chem. Inf. Model. 51: 408-19.
100. Sato T, Honma T, Yokoyama S,. 2010. Combining machine learning and pharmacophore-based interaction fingerprint for in silico screening. J. Chem. Inf. Model. 50: 170-85.
101. Marsh L,. 2011. Prediction of ligand binding using an approach designed to accommodate diversity in protein-ligand interactions. PLoS One 6: e23215.
102. Kramer C, Gedeck P,. 2010. Leave-cluster-out cross-validation is appropriate for scoring functions derived from diverse protein data sets. J. Chem. Inf. Model. 50: 1961-9.
103. Li L, Wang B, Meroueh SO,. 2011. Support vector regression scoring of receptor-ligand complexes for rank-ordering and virtual screening of chemical libraries. J. Chem. Inf. Model. 51: 2132-8.
104. Li L, Khanna M, Jo I, Wang F, Ashpole NM, Hudmon A, Meroueh SO,. 2011. Target-specific support vector machine scoring in structure-based virtual screening: computational validation, in vitro testing in kinases, and effects on lung cancer cell proliferation. J. Chem. Inf. Model. 51: 755-9.
105. Tang YT, Marshall GR,. 2011. PHOENIX: a scoring function for affinity prediction derived using high-resolution crystal structures and measurements. J. Chem. Inf. Model. 51: 214-28.
106. Shen Q, Xiong B, Zheng M, Luo X, Luo C, Liu X, Du Y, Li J, Zhu W, Shen J, Jiang H,. 2011. Knowledge-based scoring functions in drug design: 2. Can the knowledge base be enriched? J. Chem. Inf. Model. 51: 386-97.
107. Wang JC, Lin JH, Chen CM, Perryman AL, Olson AJ,. 2011. Robust scoring functions for protein-ligand interactions with quantum chemical charge models. J. Chem. Inf. Model. 51: 2528-37.
108. Zhou T, Caflisch A,. 2010. High-throughput virtual screening using quantum mechanical probes: discovery of selective kinase inhibitors. ChemMedChem 5: 1007-14.
109. Fanfrlik J, Bronowska AK, Rezac J, Prenosil O, Konvalinka J, Hobza P,. 2010. A reliable docking/scoring scheme based on the semiempirical quantum mechanical PM6-DH2 method accurately covering dispersion and H-bonding: HIV-1 protease with 22 ligands. J. Phys. Chem. B 114: 12666-78.
110. Xue M, Zheng M, Xiong B, Li Y, Jiang H, Shen J,. 2010. Knowledge-based scoring functions in drug design. 1. Developing a target-specific method for kinase-ligand interactions. J. Chem. Inf. Model. 50: 1378-86.
111. Henrich S, Feierberg I, Wang T, Blomberg N, Wade RC,. 2010. Comparative binding energy analysis for binding affinity and target selectivity prediction. Proteins 78: 135-53.
112. Zsoldos Z, Reid D, Simon A, Sadjad BS, Johnson AP,. 2006. eHiTS: an innovative approach to the docking and scoring function problems. Curr. Protein Pept. Sci. 7: 421-35.
113. Ravitz O, Zsoldos Z, Simon A,. 2011. Improving molecular docking through eHiTS' tunable scoring function. J. Comput. Aided Mol. Des. 25: 1033-51.
114. Li Y, Shen J, Sun X, Li W, Liu G, Tang Y,. 2010. Accuracy assessment of protein-based docking programs against RNA targets. J. Chem. Inf. Model. 50: 1134-46.
115. Fukunishi Y,. 2010. Post processing of protein-compound docking for fragment-based drug discovery (FBDD): In-silico structure-based drug screening and ligand-binding pose prediction. Curr. Top. Med. Chem. 10: 680-94.
116. Konteatis ZD,. 2010. In silico fragment-based drug design. Expert Opin. Drug Discov. 5: 1047-1065.
117. Chen Y, Pohlhaus DT,. 2010. In silico docking and scoring of fragments. Drug Discov. Today: Technologies 7: e149-e156.
118. Sandor M, Kiss R, Keseru GM,. 2010. Virtual fragment docking by Glide: a validation study on 190 protein-fragment complexes. J. Chem. Inf. Model. 50: 1165-72.
119. Verdonk ML, Giangreco I, Hall RJ, Korb O, Mortenson PN, Murray CW,. 2011. Docking performance of fragments and druglike compounds. J. Med. Chem. 54: 5422-31.
120. vFavia AD, Bottegoni G, Nobeli I, Bisignano P, Cavalli A,. 2011. SERAPhiC: a benchmark for in silico fragment-based drug design. J. Chem. Inf. Model. 51: 2882-96.
121. Huang N, Jacobson MP,. 2010. Binding-site assessment by virtual fragment screening. PLoS One 5: e10109.
122. Ward RA,. 2010. Using protein-ligand docking to assess the chemical tractability of inhibiting a protein target. J. Mol. Model. 16: 1833-43.
123. Lin FY, Tseng YJ,. 2011. Structure-based fragment hopping for lead optimization using predocked fragment database. J. Chem. Inf. Model. 51: 1703-15.
124. Huang D, Caflisch A,. 2010. Library screening by fragment-based docking. J. Mol. Recognit. 23: 183-93.
125. Englert L, Silber K, Steuber H, Brass S, Over B, Gerber HD, Heine A, Diederich WE, Klebe G,. 2010. Fragment-based lead discovery: screening and optimizing fragments for thermolysin inhibition. ChemMedChem 5: 930-40.
126. de Graaf C, Kooistra AJ, Vischer HF, Katritch V, Kuijer M, Shiroishi M, Iwata S, Shimamura T, Stevens RC, de Esch IJ, Leurs R,. 2011. Crystal structure-based virtual screening for fragment-like ligands of the human histamine H(1) receptor. J. Med. Chem. 54: 8195-206.
127. Rognan D,. 2010. Structure-based approaches to target fishing and ligand profiling. Mol. Inf. 29: 176-187.
128. Hui-fang L, Qing S, Jian Z, Wei F,. 2010. Evaluation of various inverse docking schemes in multiple targets identification. J. Mol. Graph. Model. 29: 326-30.
129. Grinter SZ, Liang Y, Huang SY, Hyder SM, Zou X,. 2011. An inverse docking approach for identifying new potential anti-cancer targets. J. Mol. Graph. Model. 29: 795-9.
130. Zheng R, Chen TS, Lu T,. 2011. A comparative reverse docking strategy to identify potential antineoplastic targets of tea functional components and binding mode. Int. J. Mol. Sci. 12: 5200-12.
131. Novikov FN, Stroylov VS, Stroganov OV, Chilov GG,. 2010. Improving performance of docking-based virtual screening by structural filtration. J. Mol. Model. 16: 1223-30.
132. Fukunishi Y, Mizukoshi Y, Takeuchi K, Shimada I, Takahashi H, Nakamura H,. 2011. Protein-ligand docking guided by ligand pharmacophore-mapping experiment by NMR. J. Mol. Graph. Model. 31: 20-7.
133. Agostino M, Sandrin MS, Thompson PE, Yuriev E, Ramsland PA,. 2010. Identification of preferred carbohydrate binding modes in xenoreactive antibodies by combining conformational filters and binding site maps. Glycobiology 20: 724-735.
134. Agostino M, Yuriev E, Ramsland P,. 2011. A computational approach for exploring carbohydrate recognition by lectins in innate immunity. Front. Immun. 2: 23.
135. Agostino M, Yuriev E, Ramsland PA,. 2012. Antibody recognition of cancer-related gangliosides and their mimics investigated using in silico site mapping. PLoS One 7: e35457.
136. Agostino M, Sandrin MS, Thompson PE, Yuriev E, Ramsland P,. 2009. In silico analysis of antibody-carbohydrate interactions and its application to xenoreactive antibodies. Mol. Immunol. 47: 233-246.
137. Agostino M, Jene C, Boyle T, Ramsland PA, Yuriev E,. 2009. Molecular docking of carbohydrate ligands to antibodies: Structural validation against crystal structures. J. Chem. Inf. Model. 49: 2749-2760.
138. Agostino M, Sandrin MS, Thompson PE, Ramsland PA, Yuriev E,. 2011. Peptide inhibitors of xenoreactive antibodies mimic the interaction profile of the native carbohydrate antigens. Biopolymers 96: 193-206.
139. Bouvier G, Evrard-Todeschi N, Girault JP, Bertho G,. 2010. Automatic clustering of docking poses in virtual screening process using self-organizing map. Bioinformatics 26: 53-60.
140. Spitzmuller A, Velec HFG, Klebe G,. 2011. MiniMuDS: A New Optimizer using Knowledge-Based Potentials Improves Scoring of Docking Solutions. J. Chem. Inf. Model. 51: 1423-1430.
141. Burger SK, Thompson DC, Ayers PW,. 2011. Quantum mechanics/molecular mechanics strategies for docking pose refinement: distinguishing between binders and decoys in cytochrome C peroxidase. J. Chem. Inf. Model. 51: 93-101.
142. Balius TE, Mukherjee S, Rizzo RC,. 2011. Implementation and evaluation of a docking-rescoring method using molecular footprint comparisons. J. Comput. Chem. 32: 2273-2289.
143. Taha MO, Habash M, Al-Hadidi Z, Al-Bakri A, Younis K, Sisan S,. 2011. Docking-based comparative intermolecular contacts analysis as new 3-D QSAR concept for validating docking studies and in silico screening: NMT and GP inhibitors as case studies. J. Chem. Inf. Model. 51: 647-69.
144. Shahid M, Kasam V, Hofmann-Apitius M,. 2010. An improved weighted-residue profile based method of using protein-ligand interaction information in increasing hits selection from virtual screening: A study on virtual screening of human GPCR A2A receptor antagonists. Mol. Inf. 29: 781-791.
145. Garcia-Sosa AT, Hetenyi C, Maran U,. 2010. Drug efficiency indices for improvement of molecular docking scoring functions. J. Comput. Chem. 31: 174-84.
146. Malmstrom RD, Watowich SJ,. 2011. Using free energy of binding calculations to improve the accuracy of virtual screening predictions. J. Chem. Inf. Model. 51: 1648-55.
147. Cavasotto CN,. 2011. Homology models in docking and high-throughput docking. Curr. Top. Med. Chem. 11: 1528-34.
148. Bordogna A, Pandini A, Bonati L,. 2011. Predicting the accuracy of protein-ligand docking on homology models. J. Comput. Chem. 32: 81-98.
149. Tang H, Wang XS, Hsieh JH, Tropsha A,. 2012. Do crystal structures obviate the need for theoretical models of GPCRs for structure-based virtual screening? Proteins 80: 1503-21.
150. 3 receptor in complex with eticlopride. Proteins 79: 1695-703.
151. Kufareva I, Rueda M, Katritch V, Stevens RC, Abagyan R,. 2011. Status of GPCR modeling and docking as reflected by community-wide GPCR Dock 2010 assessment. Structure 19: 1108-26.
152. Mahasenan KV, Pavlovicz RE, Henderson BJ, Gonzalez-Cestari TF, Yi BN, McKay DB, Li CL,. 2011. Discovery of Novel alpha 4 beta 2 Neuronal Nicotinic Receptor Modulators through Structure-Based Virtual Screening. ACS Med. Chem. Lett. 2: 855-860.
153. Bland ND, Wang C, Tallman C, Gustafson AE, Wang Z, Ashton TD, Ochiana SO, McAllister G, Cotter K, Fang AP, Gechijian L, Garceau N, Gangurde R, Ortenberg R, Ondrechen MJ, Campbell RK, Pollastri MP,. 2011. Pharmacological validation of Trypanosoma brucei phosphodiesterases B1 and B2 as druggable targets for African sleeping sickness. J. Med. Chem. 54: 8188-94.
154. He S, Lai L,. 2011. Molecular docking and competitive binding study discovered different binding modes of microsomal prostaglandin E synthase-1 inhibitors. J. Chem. Inf. Model. 51: 3254-61.
155. Motto I, Bordogna A, Soshilov AA, Denison MS, Bonati L,. 2011. New aryl hydrocarbon receptor homology model targeted to improve docking reliability. J. Chem. Inf. Model. 51: 2868-81.
156. Laurini E, Dal Col V, Mamolo MG, Zampieri D, Posocco P, Fermeglia M, Vio L, Pricl S,. 2011. Homology Model and Docking-Based Virtual Screening for Ligands of the sigma(1) Receptor. ACS Med. Chem. Lett. 2: 834-839.
157. Stuart BG, Coxon JM, Morton JD, Abell AD, McDonald DQ, Aitken SG, Jones MA, Bickerstaffe R,. 2011. Molecular modeling: a search for a calpain inhibitor as a new treatment for cataractogenesis. J. Med. Chem. 54: 7503-22.
158. Li S, Zhou Y, Lu W, Zhong Y, Song W, Liu K, Huang J, Zhao Z, Xu Y, Liu X, Li H,. 2011. Identification of inhibitors against p90 ribosomal S6 kinase 2 (RSK2) through structure-based virtual screening with the inhibitor-constrained refined homology model. J. Chem. Inf. Model. 51: 2939-47.
159. Park H, Chi O, Kim J, Hong S,. 2011. Identification of novel inhibitors of tropomyosin-related kinase A through the structure-based virtual screening with homology-modeled protein structure. J. Chem. Inf. Model. 51: 2986-93.
160. Tuccinardi T, Botta M, Giordano A, Martinelli A,. 2010. Protein kinases: docking and homology modeling reliability. J. Chem. Inf. Model. 50: 1432-41.
161. Brylinski M, Skolnick J,. 2010. Comprehensive structural and functional characterization of the human kinome by protein structure modeling and ligand virtual screening. J. Chem. Inf. Model. 50: 1839-54.
162. Wada M, Kanamori E, Nakamura H, Fukunishi Y,. 2011. Selection of in silico drug screening results for G-protein-coupled receptors by using universal active probes. J. Chem. Inf. Model. 51: 2398-407.
163. Kim SK, Fristrup P, Abrol R, Goddard WA, III,. 2011. Structure-based prediction of subtype selectivity of histamine H3 receptor selective antagonists in clinical trials. J. Chem. Inf. Model. 51: 3262-74.
164. Dilly S, Scuvee-Moreau J, Wouters J, Liegeois JF,. 2011. The 5-HT(1A) agonism potential of substituted piperazine-ethyl-amide derivatives is conserved in the hexyl homologues: molecular modeling and pharmacological evaluation. J. Chem. Inf. Model. 51: 2961-6.
165. Pecic S, Makkar P, Chaudhary S, Reddy BV, Navarro HA, Harding WW,. 2010. Affinity of aporphines for the human 5-HT2A receptor: insights from homology modeling and molecular docking studies. Bioorg. Med. Chem. 18: 5562-75.
166. Cooper DR, Porebski PJ, Chruszcz M, Minor W,. 2011. X-ray crystallography: Assessment and validation of protein-small molecule complexes for drug discovery. Expert Opin. Drug Discov. 6: 771-782.
167. Malde AK, Mark AE,. 2011. Challenges in the determination of the binding modes of non-standard ligands in X-ray crystal complexes. J. Comput. Aided Mol. Des. 25: 1-12.
168. Mukherjee S, Balius TE, Rizzo RC,. 2010. Docking validation resources: protein family and ligand flexibility experiments. J. Chem. Inf. Model. 50: 1986-2000.
169. Lang PT, Brozell SR, Mukherjee S, Pettersen EF, Meng EC, Thomas V, Rizzo RC, Case DA, James TL, Kuntz ID,. 2009. DOCK 6: combining techniques to model RNA-small molecule complexes. RNA 15: 1219-30.
170. Osolodkin DI, Palyulin VA, Zefirov NS,. 2011. Structure-based virtual screening of glycogen synthase kinase 3beta inhibitors: analysis of scoring functions applied to large true actives and decoy sets. Chem. Biol. Drug Des. 78: 378-90.
171. Katritch V, Jaakola VP, Lane JR, Lin J, Ijzerman AP, Yeager M, Kufareva I, Stevens RC, Abagyan R,. 2010. Structure-based discovery of novel chemotypes for adenosine A(2A) receptor antagonists. J. Med. Chem. 53: 1799-809.
172. Carlsson J, Yoo L, Gao ZG, Irwin JJ, Shoichet BK, Jacobson KA,. 2010. Structure-based discovery of A2A adenosine receptor ligands. J. Med. Chem. 53: 3748-55.
173. 3 receptor homology model and crystal structure. Nat. Chem. Biol. 7: 769-78.
174. Dong M, Lam PC, Pinon DI, Hosohata K, Orry A, Sexton PM, Abagyan R, Miller LJ,. 2011. Molecular basis of secretin docking to its intact receptor using multiple photolabile probes distributed throughout the pharmacophore. J. Biol. Chem. 286: 23888-99.
175. Miller LJ, Chen Q, Lam PC, Pinon DI, Sexton PM, Abagyan R, Dong M,. 2011. Refinement of glucagon-like peptide 1 docking to its intact receptor using mid-region photolabile probes and molecular modeling. J. Biol. Chem. 286: 15895-907.
176. Dong M, Lam PC, Pinon DI, Orry A, Sexton PM, Abagyan R, Miller LJ,. 2010. Secretin occupies a single protomer of the homodimeric secretin receptor complex: insights from photoaffinity labeling studies using dual sites of covalent attachment. J. Biol. Chem. 285: 9919-31.
177. Ripphausen P, Nisius B, Peltason L, Bajorath J,. 2010. Quo vadis, virtual screening? A comprehensive survey of prospective applications. J. Med. Chem. 53: 8461-7.
178. Abid OUR, Babar TM, Ali FI, Ahmed S, Wadood A, Rama NH, Uddin R, Zaheer-ul-Haq, KA, Choudhary MI,. 2010. Identification of novel urease inhibitors by high-throughput virtual and in vitro screening. ACS Med. Chem. Lett. 1: 145-149.
179. Wallach I, Lilien R,. 2011. Virtual decoy sets for molecular docking benchmarks. J. Chem. Inf. Model. 51: 196-202.
180. Wallach I, Jaitly N, Nguyen K, Schapira M, Lilien R,. 2011. Normalizing molecular docking rankings using virtually generated decoys. J. Chem. Inf. Model. 51: 1817-30.
181. Vogel SM, Bauer MR, Boeckler FM,. 2011. DEKOIS: demanding evaluation kits for objective in silico screening - a versatile tool for benchmarking docking programs and scoring functions. J. Chem. Inf. Model. 51: 2650-65.
182. Vistoli G, Pedretti A, Mazzolari A, Testa B,. 2010. Homology modeling and metabolism prediction of human carboxylesterase-2 using docking analyses by GriDock: a parallelized tool based on AutoDock 4.0. J. Comput. Aided Mol. Des. 24: 771-87.
183. Collignon B, Schulz R, Smith JC, Baudry J,. 2011. Task-parallel message passing interface implementation of AutoDock4 for docking of very large databases of compounds using high-performance super-computers. J. Comput. Chem. 32: 1202-1209.
184. Abreu RM, Froufe HJ, Queiroz MJ, Ferreira IC,. 2010. MOLA: a bootable, self-configuring system for virtual screening using AutoDock4/Vina on computer clusters. J. Cheminform. 2: 10.
185. Prakhov ND, Chernorudskiy AL, Gainullin MR,. 2010. VSDocker: a tool for parallel high-throughput virtual screening using AutoDock on Windows-based computer clusters. Bioinformatics 26: 1374-5.
186. Norgan AP, Coffman PK, Kocher JP, Katzmann DJ, Sosa CP,. 2011. Multilevel parallelization of AutoDock 4.2. J Cheminformatics 3: 12.
187. Korb O, Stutzle T, Exner TE,. 2011. Accelerating molecular docking calculations using graphics processing units. J. Chem. Inf. Model. 51: 865-76.
188. Yang H, Zhou Q, Li B, Wang Y, Luan Z, Qian D, Li H,. 2010. GPU acceleration of Dock6's Amber scoring computation. Adv. Exp. Med. Biol. 680: 497-511.
189. Wei D, Zheng H, Su N, Deng M, Lai L,. 2010. Binding energy landscape analysis helps to discriminate true hits from high-scoring decoys in virtual screening. J. Chem. Inf. Model. 50: 1855-64.
190. Brooijmans N, Cross JB, Humblet C,. 2010. Biased retrieval of chemical series in receptor-based virtual screening. J. Comput. Aided Mol. Des. 24: 1053-62.
191. Swann SL, Brown SP, Muchmore SW, Patel H, Merta P, Locklear J, Hajduk PJ,. 2011. A unified, probabilistic framework for structure- and ligand-based virtual screening. J. Med. Chem. 54: 1223-32.
192. Wiggers HJ, Rocha JR, Cheleski J, Montanari CA,. 2011. Integration of Ligand- and Target-Based Virtual Screening for the Discovery of Cruzain Inhibitors. Mol. Inf. 30: 565-578.
193. Kruger DM, Evers A,. 2010. Comparison of structure- and ligand-based virtual screening protocols considering hit list complementarity and enrichment factors. ChemMedChem 5: 148-58.
194. Carlson HA, Dunbar JB, Jr., 2011. A call to arms: what you can do for computational drug discovery. J. Chem. Inf. Model. 51: 2025-6.
195. Dunbar JB, Jr., Smith RD, Yang CY, Ung PM, Lexa KW, Khazanov NA, Stuckey JA, Wang S, Carlson HA,. 2011. CSAR benchmark exercise of 2010: selection of the protein-ligand complexes. J. Chem. Inf. Model. 51: 2036-46.
196. Huang SY, Zou X,. 2011. Construction and test of ligand decoy sets using MDock: Community Structure-Activity Resource benchmarks for binding mode prediction. J. Chem. Inf. Model. 51: 2107-14.
197. Smith RD, Dunbar JB, Jr., Ung PM, Esposito EX, Yang CY, Wang S, Carlson HA,. 2011. CSAR benchmark exercise of 2010: combined evaluation across all submitted scoring functions. J. Chem. Inf. Model. 51: 2115-31.
198. Novikov FN, Zeifman AA, Stroganov OV, Stroylov VS, Kulkov V, Chilov GG,. 2011. CSAR scoring challenge reveals the need for new concepts in estimating protein-ligand binding affinity. J. Chem. Inf. Model. 51: 2090-6.
199. Huang SY, Zou X,. 2011. Scoring and lessons learned with the CSAR benchmark using an improved iterative knowledge-based scoring function. J. Chem. Inf. Model. 51: 2097-106.
200. Sulea T, Cui Q, Purisima EO,. 2011. Solvated interaction energy (SIE) for scoring protein-ligand binding affinities. 2. Benchmark in the CSAR-2010 scoring exercise. J. Chem. Inf. Model. 51: 2066-81.
201. Rahaman O, Estrada TP, Doren DJ, Taufer M, Brooks CL, III, Armen RS,. 2011. Evaluation of several two-step scoring functions based on linear interaction energy, effective ligand size, and empirical pair potentials for prediction of protein-ligand binding geometry and free energy. J. Chem. Inf. Model. 51: 2047-65.
202. Li X, Li Y, Cheng T, Liu Z, Wang R,. 2010. Evaluation of the performance of four molecular docking programs on a diverse set of protein-ligand complexes. J. Comput. Chem. 31: 2109-25.
203. Giganti D, Guillemain H, Spadoni JL, Nilges M, Zagury JF, Montes M,. 2010. Comparative evaluation of 3D virtual ligand screening methods: impact of the molecular alignment on enrichment. J. Chem. Inf. Model. 50: 992-1004.
204. Zaheer ul H, Halim SA, Uddin R, Madura JD,. 2010. Benchmarking docking and scoring protocol for the identification of potential acetylcholinesterase inhibitors. J. Mol. Graph. Model. 28: 870-82.
205. Lape M, Elam C, Paula S,. 2010. Comparison of current docking tools for the simulation of inhibitor binding by the transmembrane domain of the sarco/endoplasmic reticulum calcium ATPase. Biophys. Chem. 150: 88-97.
206. Ali HI, Fujita T, Akaho E, Nagamatsu T,. 2010. A comparative study of AutoDock and PMF scoring performances, and SAR of 2-substituted pyrazolotriazolopyrimidines and 4-substituted pyrazolopyrimidines as potent xanthine oxidase inhibitors. J. Comput. Aided Mol. Des. 24: 57-75.
207. Singh T, Biswas D, Jayaram B,. 2011. AADS - an automated active site identification, docking, and scoring protocol for protein targets based on physicochemical descriptors. J. Chem. Inf. Model. 51: 2515-27.
208. Durrant JD, Friedman AJ, McCammon JA,. 2011. CrystalDock: a novel approach to fragment-based drug design. J. Chem. Inf. Model. 51: 2573-80.
209. Sandeep G, Nagasree KP, Hanisha M, Kumar MM,. 2011. AUDocker LE: A GUI for virtual screening with AutoDock Vina. BMC Res. Notes 4: 445.
210. Bullock CW, Jacob RB, McDougal OM, Hampikian G, Andersen T,. 2010. DockoMatic-automated ligand creation and docking. BMC Res. Notes 3: 289.
211. Jacob RB, Bullock CW, Andersen T, McDougal OM,. 2011. DockoMatic: Automated peptide analog creation for high throughput virtual screening. J. Comput. Chem. 32: 2936-2941.
212. Hsu KC, Chen YF, Lin SR, Yang JM,. 2011. iGEMDOCK: a graphical environment of enhancing GEMDOCK using pharmacological interactions and post-screening analysis. BMC Bioinformatics 12 (1): S33.
213. Seeliger D, de Groot BL,. 2010. Ligand docking and binding site analysis with PyMOL and AutoDock/Vina. J. Comput. Aided Mol. Des. 24: 417-22.
214. Lill MA, Danielson ML,. 2011. Computer-aided drug design platform using PyMOL. J. Comput. Aided Mol. Des. 25: 13-9.
215. Baba N, Akaho E,. 2011. VSDK: Virtual screening of small molecules using AutoDock Vina on Windows platform. Bioinformation 6: 387-8.
216. Cabrera AC, Gil-Redondo R, Perona A, Gago F, Morreale A,. 2011. VSDMIP 1.5: an automated structure- and ligand-based virtual screening platform with a PyMOL graphical user interface. J. Comput. Aided Mol. Des. 25: 813-24.
217. Tsai TY, Chang KW, Chen CY,. 2011. iScreen: world's first cloud-computing web server for virtual screening and de novo drug design based on TCM database@Taiwan. J. Comput. Aided Mol. Des. 25: 525-31.
218. Grosdidier A, Zoete V, Michielin O,. 2011. SwissDock, a protein-small molecule docking web service based on EADock DSS. Nucleic Acids Res. 39: W270-7.