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Mini-Reviews in Medicinal Chemistry

Author(s): Mayara dos Santos Maia, Gabriela Cristina Soares Rodrigues, Andreza Barbosa Silva Cavalcanti, Luciana Scotti and Marcus Tullius Scotti*

DOI: 10.2174/1389557520666200204121129

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Consensus Analyses in Molecular Docking Studies Applied to Medicinal Chemistry

Page: [1322 - 1340] Pages: 19

  • * (Excluding Mailing and Handling)

Abstract

The increasing number of computational studies in medicinal chemistry involving molecular docking has put the technique forward as promising in Computer-Aided Drug Design. Considering the main method in the virtual screening based on the structure, consensus analysis of docking has been applied in several studies to overcome limitations of algorithms of different programs and mainly to increase the reliability of the results and reduce the number of false positives. However, some consensus scoring strategies are difficult to apply and, in some cases, are not reliable due to the small number of datasets tested. Thus, for such a methodology to be successful, it is necessary to understand why, when and how to use consensus docking. Therefore, the present study aims to present different approaches to docking consensus, applications, and several scoring strategies that have been successful and can be applied in future studies.

Keywords: Molecular docking, consensus analysis, medicinal chemistry, virtual screening, consensus scoring strategies, statistical models.

Graphical Abstract

[1]
Gupta, M.; Sharma, R.; Kumar, A. Docking techniques in pharmacology: How much promising? Comput. Biol. Chem., 2018, 76, 210-217.
[http://dx.doi.org/10.1016/j.compbiolchem.2018.06.005] [PMID: 30067954]
[2]
Poli, G.; Martinelli, A.; Tuccinardi, T. Reliability analysis and optimization of the consensus docking approach for the development of virtual screening studies. J. Enzyme Inhib. Med. Chem., 2016, 31(sup2), 167-173.
[http://dx.doi.org/10.1080/14756366.2016.1193736] [PMID: 27311630]
[3]
Meng, X-Y.; Zhang, H-X.; Mezei, M.; Cui, M. Molecular docking: A powerful approach for structure-based drug discovery. Curr Comput Aided Drug Des, 2011, 7(2), 146-157.
[http://dx.doi.org/10.2174/157340911795677602] [PMID: 21534921]
[4]
Onawole, A.T.; Kolapo, T.U.; Sulaiman, K.O.; Adegoke, R.O. Structure based virtual screening of the Ebola virus trimeric glycoprotein using consensus scoring. Comput. Biol. Chem., 2018, 72(72), 170-180.
[http://dx.doi.org/10.1016/j.compbiolchem.2017.11.006] [PMID: 29361403]
[5]
Charifson, P.S.; Corkery, J.J.; Murcko, M.A.; Walters, W.P. Consensus scoring: A method for obtaining improved hit rates from docking databases of three-dimensional structures into proteins. J. Med. Chem., 1999, 42(25), 5100-5109.
[http://dx.doi.org/10.1021/jm990352k] [PMID: 10602695]
[6]
Wang, R.; Wang, S. How does consensus scoring work for virtual library screening? An idealized computer experiment. J. Chem. Inf. Comput. Sci., 2001, 41(5), 1422-1426.
[http://dx.doi.org/10.1021/ci010025x] [PMID: 11604043]
[7]
Torres, P.H.M.; Sodero, A.C.R.; Jofily, P.; Silva-Jr, F.P. Key topics in molecular docking for drug design. Int. J. Mol. Sci., 2019, 20(18), 4574.
[http://dx.doi.org/10.3390/ijms20184574] [PMID: 31540192]
[8]
Du, J.; Bleylevens, I.W.M.; Bitorina, A.V.; Wichapong, K.; Nicolaes, G.A.F. Optimization of compound ranking for structure-based virtual ligand screening using an established FRED-Surflex consensus approach. Chem. Biol. Drug Des., 2014, 83(1), 37-51.
[http://dx.doi.org/10.1111/cbdd.12202] [PMID: 23941463]
[9]
Houston, D.R.; Walkinshaw, M.D. Consensus docking: Improving the reliability of docking in a virtual screening context. J. Chem. Inf. Model., 2013, 53(2), 384-390.
[http://dx.doi.org/10.1021/ci300399w] [PMID: 23351099]
[10]
Mavrogeni, M.E.; Pronios, F.; Zareifi, D.; Vasilakaki, S.; Lozach, O.; Alexopoulos, L.; Meijer, L.; Myrianthopoulos, V.; Mikros, E. A facile consensus ranking approach enhances virtual screening robustness and identifies a cell-active DYRK1α inhibitor. Future Med. Chem., 2018, 10(20), 2411-2430.
[http://dx.doi.org/10.4155/fmc-2018-0198] [PMID: 30325204]
[11]
Voet, A.R.D.; Kumar, A.; Berenger, F.; Zhang, K.Y.J. Combining in silico and in cerebro approaches for virtual screening and pose prediction in SAMPL4. J. Comput. Aided Mol. Des., 2014, 28(4), 363-373.
[http://dx.doi.org/10.1007/s10822-013-9702-2] [PMID: 24446075]
[12]
Bajusz, D.; Rácz, A.; Héberger, K. Comparison of data fusion methods as consensus scores for ensemble docking. Molecules, 2019, 24(15), 2690.
[http://dx.doi.org/10.3390/molecules24152690] [PMID: 31344902]
[13]
Bowen, L.R.; Li, D.J.; Nola, D.T.; Anderson, M.O.; Heying, M.; Groves, A.T.; Eagon, S. Identification of potential Zika virus NS2B-NS3 protease inhibitors via docking, molecular dynamics and consensus scoring-based virtual screening. J. Mol. Model., 2019, 25(7), 194.
[http://dx.doi.org/10.1007/s00894-019-4076-6] [PMID: 31209577]
[14]
Salmas, R.E.; Seeman, P.; Aksoydan, B.; Erol, I.; Kantarcioglu, I.; Stein, M.; Yurtsever, M.; Durdagi, S. Analysis of the glutamate agonist ly404,039 binding to nonstatic dopamine receptor D2 dimer structures and consensus docking. ACS Chem. Neurosci., 2017, 8(6), 1404-1415.
[http://dx.doi.org/10.1021/acschemneuro.7b00070] [PMID: 28272861]
[15]
Wang, D.; Cui, C.; Ding, X.; Xiong, Z.; Zheng, M.; Luo, X.; Jiang, H.; Chen, K. Improving the virtual screening ability of target specific scoring functions using deep learning methods. Front. Pharmacol., 2019, 10, 924.
[http://dx.doi.org/10.3389/fphar.2019.00924] [PMID: 31507420]
[16]
Kalid, O.; Toledo Warshaviak, D.; Shechter, S.; Sherman, W.; Shacham, S. Consensus Induced Fit Docking (cIFD): Methodology, validation, and application to the discovery of novel Crm1 inhibitors. J. Comput. Aided Mol. Des., 2012, 26(11), 1217-1228.
[http://dx.doi.org/10.1007/s10822-012-9611-9] [PMID: 23053738]
[17]
Preto, J.; Gentile, F. Assessing and improving the performance of consensus docking strategies using the DockBox package. J. Comput. Aided Mol. Des., 2019, 33(9), 817-829.
[http://dx.doi.org/10.1007/s10822-019-00227-7] [PMID: 31578656]
[18]
Perez-Castillo, Y.; Sotomayor-Burneo, S.; Jimenes-Vargas, K.; Gonzalez-Rodriguez, M.; Cruz-Monteagudo, M.; Armijos-Jaramillo, V.; Cordeiro, M.N.D.S.; Borges, F.; Sánchez-Rodríguez, A.; Tejera, E. CompScore: Boosting structure-based virtual screening performance by incorporating docking scoring function components into consensus scoring. J. Chem. Inf. Model., 2019, 59(9), 3655-3666.
[http://dx.doi.org/10.1021/acs.jcim.9b00343] [PMID: 31449403]
[19]
Vilar, S.; Costanzi, S. Predicting the biological activities through QSAR analysis and docking-based scoring. Methods Mol. Biol., 2012, 914, 271-284.
[http://dx.doi.org/10.1007/978-1-62703-023-6_16] [PMID: 22976034]
[20]
Chermak, E.; De Donato, R.; Lensink, M.F.; Petta, A.; Serra, L.; Scarano, V.; Cavallo, L.; Oliva, R. Introducing a clustering step in a consensus approach for the scoring of protein-protein docking models. PLoS One, 2016, 11(11), e0166460.
[http://dx.doi.org/10.1371/journal.pone.0166460] [PMID: 27846259]
[21]
Ren, X.; Shi, Y.S.; Zhang, Y.; Liu, B.; Zhang, L.H.; Peng, Y.B.; Zeng, R. Novel consensus docking strategy to improve ligand pose prediction. J. Chem. Inf. Model., 2018, 58(8), 1662-1668.
[http://dx.doi.org/10.1021/acs.jcim.8b00329] [PMID: 30044626]
[22]
Spinello, A.; Vecile, E.; Abbate, A.; Dobrina, A.; Magistrato, A. How can interleukin-1 receptor antagonist modulate distinct cell death pathways? J. Chem. Inf. Model., 2019, 59(1), 351-359.
[http://dx.doi.org/10.1021/acs.jcim.8b00565] [PMID: 30586302]
[23]
Pierce, B.G.; Wiehe, K.; Hwang, H.; Kim, B.H.; Vreven, T.; Weng, Z. ZDOCK server: Interactive docking prediction of protein-protein complexes and symmetric multimers. Bioinformatics, 2014, 30(12), 1771-1773.
[http://dx.doi.org/10.1093/bioinformatics/btu097] [PMID: 24532726]
[24]
Kozakov, D.; Hall, D.R.; Xia, B.; Porter, K.A.; Padhorny, D.; Yueh, C.; Beglov, D.; Vajda, S. The ClusPro web server for protein-protein docking. Nat. Protoc., 2017, 12(2), 255-278.
[http://dx.doi.org/10.1038/nprot.2016.169] [PMID: 28079879]
[25]
Jiménez-García, B.; Pons, C.; Fernández-Recio, J. pyDockWEB: A web server for rigid-body protein-protein docking using electrostatics and desolvation scoring. Bioinformatics, 2013, 29(13), 1698-1699.
[http://dx.doi.org/10.1093/bioinformatics/btt262] [PMID: 23661696]
[26]
Yan, Y.; Zhang, D.; Zhou, P.; Li, B.; Huang, S.Y. HDOCK: A web server for protein-protein and protein-DNA/RNA docking based on a hybrid strategy. Nucleic Acids Res., 2017, 45(W1), W365-W373.
[http://dx.doi.org/10.1093/nar/gkx407] [PMID: 28521030]
[27]
Lyskov, S.; Gray, J. J. The RosettaDock Server for Local Protein-Protein Docking. Nucleic Acids Res., 2008, 36(Web Server issue). , 233-238.
[http://dx.doi.org/10.1093/nar/gkn216]
[28]
Kausar, S.; Asif, M.; Bibi, N.; Rashid, S. Comparative molecular docking analysis of cytoplasmic dynein light chain DYNLL1 with pilin to explore the molecular mechanism of pathogenesis caused by Pseudomonas aeruginosa PAO. PLoS One, 2013, 8(10), e76730.
[http://dx.doi.org/10.1371/journal.pone.0076730] [PMID: 24098557]
[29]
Morris, G.M.; Huey, R.; Lindstrom, W.; Sanner, M.F.; Belew, R.K.; Goodsell, D.S.; Olson, A.J. AutoDock4 and AutoDock- Tools4: Automated docking with selective receptor flexibility. J. Comput. Chem., 2009, 30(16), 2785-2791.
[http://dx.doi.org/10.1002/jcc.21256] [PMID: 19399780]
[30]
Schneidman-Duhovny, D.; Inbar, Y.; Nussinov, R.; Wolfson, H.J. PatchDock and SymmDock: Servers for rigid and symmetric docking. Nucleic Acids Res., 2005, 33(Web Server issue) (Suppl. 2). , W363-7.
[http://dx.doi.org/10.1093/nar/gki481] [PMID: 15980490]
[31]
Mashiach, E.; Schneidman-Duhovny, D.; Andrusier, N.; Nussinov, R.; Wolfson, H. J. FireDock: A Web Server for Fast Interaction Refinement in Molecular Docking. Nucleic Acids Res., 2008, 36(Web Server issue). , 229-232.
[http://dx.doi.org/10.1093/nar/gkn186]
[32]
Ravikant, D.V.S.; Elber, R. PIE-efficient filters and coarse grained potentials for unbound protein-protein docking. Proteins, 2010, 78(2), 400-419.
[http://dx.doi.org/10.1002/prot.22550] [PMID: 19768784]
[33]
Mashiach, E.; Nussinov, R.; Wolfson, H.J. FiberDock: Flexible induced-fit backbone refinement in molecular docking. Proteins, 2010, 78(6), 1503-1519.
[http://dx.doi.org/10.1002/prot.22668] [PMID: 20077569]
[34]
Jaundoo, R.; Bohmann, J.; Gutierrez, G.E.; Klimas, N.; Broderick, G.; Craddock, T.J.A. Using a consensus docking approach to predict adverse drug reactions in combination drug therapies for gulf war illness. Int. J. Mol. Sci., 2018, 19(11), 1-23.
[http://dx.doi.org/10.3390/ijms19113355] [PMID: 30373189]
[35]
Trott, O.; Olson, A.J. AutoDock Vina: Improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading. J. Comput. Chem., 2010, 31(2), 455-461.
[http://dx.doi.org/10.1002/jcc.21334] [PMID: 19499576]
[36]
Repasky, M.P.; Shelley, M.; Friesner, R.A. Flexible ligand docking with Glide.Curr. Protoc. Bioinformatics; , 2007. Chapter 8, 12.
[http://dx.doi.org/10.1002/0471250953.bi0812s18] [PMID: 18428795]
[37]
Howell, D.C. Median Absolute Deviation; John Wiley & Sons: Hoboken, NJ, USA, 2014. https://doi.org/doi.org/10.1002/9781118445112.stat06232
[http://dx.doi.org/10.1002/9781118445112.stat06232]
[38]
Leherte, L.; Petit, A.; Jacquemin, D.; Vercauteren, D.P.; Laurent, A.D. Investigating cyclic peptides inhibiting CD2-CD58 interactions through molecular dynamics and molecular docking methods. J. Comput. Aided Mol. Des., 2018, 32(11), 1295-1313.
[http://dx.doi.org/10.1007/s10822-018-0172-4] [PMID: 30368623]
[39]
Koes, D.R.; Baumgartner, M.P.; Camacho, C.J. Lessons learned in empirical scoring with smina from the CSAR 2011 benchmarking exercise. J. Chem. Inf. Model., 2013, 53(8), 1893-1904.
[http://dx.doi.org/10.1021/ci300604z] [PMID: 23379370]
[40]
Jones, G.; Willett, P.; Glen, R.C.; Leach, A.R.; Taylor, R. Development and validation of a genetic algorithm for flexible docking. J. Mol. Biol., 1997, 267(3), 727-748.
[http://dx.doi.org/10.1006/jmbi.1996.0897] [PMID: 9126849]
[41]
Olsson, M.A.; García-Sosa, A.T.; Ryde, U. Binding affinities of the farnesoid X receptor in the D3R Grand Challenge 2 estimated by free-energy perturbation and docking. J. Comput. Aided Mol. Des., 2018, 32(1), 211-224.
[http://dx.doi.org/10.1007/s10822-017-0056-z] [PMID: 28879536]
[42]
FXR experimental data for the D3R Grand Challenge 2.
[43]
Cho, A.E.; Guallar, V.; Berne, B.J.; Friesner, R. Importance of accurate charges in molecular docking: Quantum mechanical/molecular mechanical (QM/MM) approach. J. Comput. Chem., 2005, 26(9), 915-931.
[http://dx.doi.org/10.1002/jcc.20222] [PMID: 15841474]
[44]
Poli, G.; Giuntini, N.; Martinelli, A.; Tuccinardi, T. Application of a FLAP-consensus docking mixed strategy for the identification of new fatty acid amide hydrolase inhibitors. J. Chem. Inf. Model., 2015, 55(3), 667-675.
[http://dx.doi.org/10.1021/ci5006806] [PMID: 25746133]
[45]
Cui, Y.H.; Chen, J.; Xu, T.; Tian, H.L. Structure-based grafting and identification of kinase-inhibitors to target mTOR signaling pathway as potential therapeutics for glioblastoma. Comput. Biol. Chem., 2015, 54, 57-65.
[http://dx.doi.org/10.1016/j.compbiolchem.2015.01.001] [PMID: 25625417]
[46]
Eldridge, M.D.; Murray, C.W.; Auton, T.R.; Paolini, G.V.; Mee, R.P. Empirical scoring functions: I. The development of a fast empirical scoring function to estimate the binding affinity of ligands in receptor complexes. J. Comput. Aided Mol. Des., 1997, 11(5), 425-445.
[http://dx.doi.org/10.1023/A:1007996124545] [PMID: 9385547]
[47]
Wang, R.; Lai, L.; Wang, S. Further development and validation of empirical scoring functions for structure-based binding affinity prediction. J. Comput. Aided Mol. Des., 2002, 16(1), 11-26.
[http://dx.doi.org/10.1023/A:1016357811882] [PMID: 12197663]
[48]
Velec, H.F.; Gohlke, H.; Klebe, G. DrugScore(CSD)-knowledge-based scoring function derived from small molecule crystal data with superior recognition rate of near-native ligand poses and better affinity prediction. J. Med. Chem., 2005, 48(20), 6296-6303.
[http://dx.doi.org/10.1021/jm050436v] [PMID: 16190756]
[49]
Zhang, C.; Liu, S.; Zhu, Q.; Zhou, Y. A knowledge-based energy function for protein-ligand, protein-protein, and protein-DNA complexes. J. Med. Chem., 2005, 48(7), 2325-2335.
[http://dx.doi.org/10.1021/jm049314d] [PMID: 15801826]
[50]
Meng, E.C.; Shoichet, B.K.; Kuntz, I.D. Automated docking with grid‐based energy evaluation. J. Comput. Chem., 1992, 13(4), 505-524.https://doi.org/https://doi.org/10.1002/jcc.540130412
[http://dx.doi.org/10.1002/jcc.540130412]
[51]
Goodsell, D.S.; Morris, G.M.; Olson, A.J. Automated docking of flexible ligands: Applications of AutoDock. J. Mol. Recognit., 1996, 9(1), 1-5.
[http://dx.doi.org/10.1002/(SICI)1099-1352(199601)9:1<1::AIDJMR241>3.0.CO;2-6] [PMID: 8723313]
[52]
Saikia, S.; Bordoloi, M.; Sarmah, R.; Kolita, B. Antiviral compound screening, peptide designing, and protein network construction of influenza a virus (strain a/Puerto Rico/8/1934 H1N1). Drug Dev. Res., 2019, 80(1), 106-124.
[http://dx.doi.org/10.1002/ddr.21475] [PMID: 30276835]
[53]
Thomsen, R.; Christensen, M.H. MolDock: A new technique for high-accuracy molecular docking. J. Med. Chem., 2006, 49(11), 3315-3321.
[http://dx.doi.org/10.1021/jm051197e] [PMID: 16722650]
[54]
Korb, O.; Stützle, T.; Exner, T.E. Empirical scoring functions for advanced protein-ligand docking with PLANTS. J. Chem. Inf. Model., 2009, 49(1), 84-96.
[http://dx.doi.org/10.1021/ci800298z] [PMID: 19125657]
[55]
Sharma, K.; Tanwar, O.; Sharma, S.; Ali, S.; Alam, M.M.; Zaman, M.S.; Akhter, M. Structural comparison of Mtb-DHFR and h-DHFR for design, synthesis and evaluation of selective non pteridine analogues as antitubercular agents. Bioorg. Chem., 2018, 80(April), 319-333.
[http://dx.doi.org/10.1016/j.bioorg.2018.04.022] [PMID: 29986181]
[56]
Wishart, D.S.; Knox, C.; Guo, A.C.; Shrivastava, S.; Hassanali, M.; Stothard, P.; Chang, Z.; Woolsey, J. DrugBank: A comprehensive resource for in silico drug discovery and exploration. Nuc. Acids Res., 2006, 34(Database issue), D668-D672.
[http://dx.doi.org/10.1093/nar/gkj067] [PMID: 16381955]
[57]
Xing, J.; Yang, L.; Li, H.; Li, Q.; Zhao, L.; Wang, X.; Zhang, Y.; Zhou, M.; Zhou, J.; Zhang, H. European journal of medicinal chemistry identi fi cation of anthranilamide derivatives as potential factor xa inhibitors: Drug design, synthesis and biological evaluation. Eur. J. Med. Chem., 2015, 95, 388-399.
[http://dx.doi.org/10.1016/j.ejmech.2015.03.052] [PMID: 25839438]
[58]
Venkatachalam, C.M.; Jiang, X.; Oldfield, T.; Waldman, M. LigandFit: A novel method for the shape-directed rapid docking of ligands to protein active sites. J. Mol. Graph. Model., 2003, 21(4), 289-307.
[http://dx.doi.org/10.1016/S1093-3263(02)00164-X] [PMID: 12479928]
[59]
Park, H.; Eom, J.W.; Kim, Y.H. Consensus scoring approach to identify the inhibitors of AMP-activated protein kinase α2 with virtual screening. J. Chem. Inf. Model., 2014, 54(7), 2139-2146.
[http://dx.doi.org/10.1021/ci500214e] [PMID: 24915156]
[60]
Rarey, M.; Kramer, B.; Lengauer, T.; Klebe, G. A fast flexible docking method using an incremental construction algorithm. J. Mol. Biol., 1996, 470-489.
[http://dx.doi.org/10.1006/jmbi.1996.0477]
[61]
Xing, J.; Li, Q.; Zhang, S.; Liu, H.; Zhao, L.; Cheng, H.; Zhang, Y.; Zhou, J.; Zhang, H. Identification of dipeptidyl peptidase IV inhibitors: Virtual screening, synthesis and biological evaluation. Chem. Biol. Drug Des., 2014, 84(3), 364-377.
[http://dx.doi.org/10.1111/cbdd.12327] [PMID: 24674599]
[62]
Lodhi, S.S.; Farmer, R.; Singh, A.K.; Jaiswal, Y.K.; Wadhwa, G. 3D structure generation, virtual screening and docking of human Ras-associated binding (Rab3A) protein involved in tumourigenesis. Mol. Biol. Rep., 2014, 41(6), 3951-3959.
[http://dx.doi.org/10.1007/s11033-014-3263-x] [PMID: 24652202]
[63]
Newcombe, J.; Chatzidaki, A.; Sheppard, T.D.; Topf, M.; Millar, N.S. Diversity of nicotinic acetylcholine receptor positive allosteric modulators revealed by mutagenesis and a revised structural model. Mol. Pharmacol., 2018, 93(2), 128-140.
[http://dx.doi.org/10.1124/mol.117.110551] [PMID: 29196491]
[64]
Aliebrahimi, S.; Montasser Kouhsari, S.; Ostad, S.N.; Arab, S.S.; Karami, L. Identification of phytochemicals targeting c-met kinase domain using consensus docking and molecular dynamics simulation studies. Cell Biochem. Biophys., 2018, 76(1-2), 135-145.
[http://dx.doi.org/10.1007/s12013-017-0821-6] [PMID: 28852971]
[65]
Onawole, A.T.; Sulaiman, K.O.; Adegoke, R.O.; Kolapo, T.U. Identification of potential inhibitors against the Zika virus using consensus scoring. J. Mol. Graph. Model., 2017, 73, 54-61.
[http://dx.doi.org/10.1016/j.jmgm.2017.01.018] [PMID: 28236744]
[66]
Hassaan, E.A.; Sigler, S.C.; Ibrahim, T.M.; Lee, K.J.; Cichon, L.K.; Gary, B.D.; Canzoneri, J.C.; Piazza, G.A.; Abadi, A.H. Mining ZINC database to discover potential phosphodiesterase 9 inhibitors using structure-based drug design approach. Med. Chem., 2016, 12(5), 472-477.
[http://dx.doi.org/10.2174/1573406412666151204002836] [PMID: 26648332]
[67]
Shah, J.J.; Khedkar, V.; Coutinho, E.C.; Mohanraj, K. Design, synthesis and evaluation of benzotriazole derivatives as novel antifungal agents. Bioorg. Med. Chem. Lett., 2015, 25(17), 3730-3737.
[http://dx.doi.org/10.1016/j.bmcl.2015.06.025] [PMID: 26117563]
[68]
Kiss, R.; Sandor, M.; Szalai, F.A. A public web service for drug discovery. J. Cheminform., 2012, 4(S1), 17.Http://Mcule.Com.
[http://dx.doi.org/10.1186/1758-2946-4-S1-P17]
[69]
Cerqueira, N.M.F.S.A.; Gesto, D.; Oliveira, E.F.; Santos-Martins, D.; Brás, N.F.; Sousa, S.F.; Fernandes, P.A.; Ramos, M.J. Receptor-based virtual screening protocol for drug discovery. Arch. Biochem. Biophys., 2015, 582, 56-67.
[http://dx.doi.org/10.1016/j.abb.2015.05.011] [PMID: 26045247]
[70]
Gangopadhyay, A.; Chakraborty, H.J.; Datta, A. Targeting the dengue β-OG with serotype-specific alkaloid virtual leads. J. Mol. Graph. Model., 2017, 73, 129-142.
[http://dx.doi.org/10.1016/j.jmgm.2017.02.018] [PMID: 28279821]
[71]
Mamidi, A.S.; Arora, P.; Surolia, A. Multivariate PLS modeling of apicomplexan fabd-ligand interaction space for mapping target specific chemical space and pharmacophore fingerprints. PLoS One, 2015, 10(11), e0141674.
[http://dx.doi.org/10.1371/journal.pone.0141674] [PMID: 26535573]
[72]
Allen, W.J.; Balius, T.E.; Mukherjee, S.; Brozell, S.R.; Moustakas, D.T.; Lang, P.T.; Case, D.A.; Kuntz, I.D.; Rizzo, R.C. DOCK 6: Impact of new features and current docking performance. J. Comput. Chem., 2015, 36(15), 1132-1156.
[http://dx.doi.org/10.1002/jcc.23905] [PMID: 25914306]
[73]
Huang, S.; Song, C.; Wang, X.; Zhang, G.; Wang, Y.; Jiang, X.; Sun, Q.; Huang, L.; Xiang, R.; Hu, Y.; Li, L.; Yang, S. Discovery of new SIRT2 inhibitors by utilizing a consensus docking/scoring strategy and structure-activity relationship analysis. J. Chem. Inf. Model., 2017, 57(4), 669-679.
[http://dx.doi.org/10.1021/acs.jcim.6b00714] [PMID: 28301150]
[74]
Sattarinezhad, E.; Bordbar, A.K.; Fani, N. Virtual screening of Piperine analogs as Survivin inhibitors and their molecular interaction analysis by using consensus docking, MD simulation, MMPB/GBSA and alanine scanning techniques. J. Biomol. Struct. Dyn., 2017, 35(8), 1824-1832.
[http://dx.doi.org/10.1080/07391102.2016.1196152] [PMID: 27576945]
[75]
Ju, Y.; Li, Z.; Deng, Y.; Tong, A.; Zhou, L.; Luo, Y. Identification of novel BACE1 inhibitors by combination of pharmacophore modeling, structure-based design and in vitro assay. Curr. Comput. Aided. Drug. Des., 2016, 12(1), 73-82.
[http://dx.doi.org/10.2174/1573409912666160222113103] [PMID: 26899408]
[76]
Berry, M.; Fielding, B.C.; Gamieldien, J. Potential Broad Spectrum Inhibitors of the Coronavirus 3CLpro: A virtual screening and structure-based drug design study. Viruses, 2015, 7(12), 6642-6660.
[http://dx.doi.org/10.3390/v7122963] [PMID: 26694449]
[77]
Wang, Y.; Ge, H.; Li, Y.; Xie, Y.; He, Y.; Xu, M.; Gu, Q.; Xu, J. Predicting dual-targeting anti-influenza agents using multi-models. Mol. Divers., 2015, 19(1), 123-134.
[http://dx.doi.org/10.1007/s11030-014-9552-4] [PMID: 25273562]
[78]
Pini, E.; Poli, G.; Tuccinardi, T.; Chiarelli, L.R.; Mori, M.; Gelain, A.; Costantino, L.; Villa, S.; Meneghetti, F.; Barlocco, D. New Chromane-Based Derivatives as inhibitors of Mycobacterium tuberculosis Salicylate Synthase (MbtI): Preliminary biological evaluation and molecular modeling studies. Molecules, 2018, 23(7), E1506.
[http://dx.doi.org/10.3390/molecules23071506] [PMID: 29933627]
[79]
Dutta, D.; Das, R.; Mandal, C.; Mandal, C. Structure-Based kinase profiling to understand the polypharmacological behavior of therapeutic molecules. J. Chem. Inf. Model., 2018, 58(1), 68-89.
[http://dx.doi.org/10.1021/acs.jcim.7b00227] [PMID: 29243930]
[80]
Oda, A.; Tsuchida, K.; Takakura, T.; Yamaotsu, N.; Hirono, S. Comparison of consensus scoring strategies for evaluating computational models of protein-ligand complexes. J. Chem. Inf. Model., 2006, 46(1), 380-391.
[http://dx.doi.org/10.1021/ci050283k] [PMID: 16426072]
[81]
Raj, U.; Kumar, H.; Varadwaj, P.K. 3D Structure generation, molecular dynamics and docking studies of IRHOM2 protein involved in cancer & rheumatoid arthritis. Curr. Comput. Aided Drug Des., 2015, 11(4), 325-335.
[http://dx.doi.org/10.2174/1573409912666151124234008] [PMID: 26603928]
[82]
Fourches, D.; Politi, R.; Tropsha, A. Target-specific native/decoy pose classifier improves the accuracy of ligand ranking in the CSAR 2013 benchmark. J. Chem. Inf. Model., 2015, 55(1), 63-71.
[http://dx.doi.org/10.1021/ci500519w] [PMID: 25521713]
[83]
Li, D.D.; Meng, X.F.; Wang, Q.; Yu, P.; Zhao, L.G.; Zhang, Z.P.; Wang, Z.Z.; Xiao, W. Consensus scoring model for the molecular docking study of mTOR kinase inhibitor. J. Mol. Graph. Model., 2018, 79, 81-87.
[http://dx.doi.org/10.1016/j.jmgm.2017.11.003] [PMID: 29154212]
[84]
Alzweiri, M.; Al-Balas, Q.; Al-Hiari, Y. Chromatographic evaluation and QSAR optimization for benzoic acid analogues against carbonic anhydrase III. J. Enzyme Inhib. Med. Chem., 2015, 30(3), 420-429.
[http://dx.doi.org/10.3109/14756366.2014.940939] [PMID: 25068727]
[85]
Ericksen, S.S.; Wu, H.; Zhang, H.; Michael, L.A.; Newton, M.A.; Hoffmann, F.M.; Wildman, S.A. Machine learning consensus scoring improves performance across targets in structure-based virtual screening. J. Chem. Inf. Model., 2017, 57(7), 1579-1590.
[http://dx.doi.org/10.1021/acs.jcim.7b00153] [PMID: 28654262]
[86]
Cotesta, S.; Giordanetto, F.; Trosset, J.Y.; Crivori, P.; Kroemer, R.T.; Stouten, P.F.W.; Vulpetti, A. Virtual screening to enrich a compound collection with CDK2 inhibitors using docking, scoring, and composite scoring models. Proteins, 2005, 60(4), 629-643.
[http://dx.doi.org/10.1002/prot.20473] [PMID: 16028223]
[87]
Liu, S.; Fu, R.; Zhou, L.H.; Chen, S.P. Application of consensus scoring and principal component analysis for virtual screening against β-secretase (BACE-1). PLoS One, 2012, 7(6), e38086.
[http://dx.doi.org/10.1371/journal.pone.0038086] [PMID: 22701601]
[88]
Jain, A.N. Surflex: Fully automatic flexible molecular docking using a molecular similarity-based search engine. J. Med. Chem., 2003, 46(4), 499-511.
[http://dx.doi.org/10.1021/jm020406h] [PMID: 12570372]
[89]
Palacio-Rodríguez, K.; Lans, I.; Cavasotto, C.N.; Cossio, P. Exponential consensus ranking improves the outcome in docking and receptor ensemble docking. Sci. Rep., 2019, 9(1), 5142.
[http://dx.doi.org/10.1038/s41598-019-41594-3] [PMID: 30914702]
[90]
Plewczynski, D.; Łaźniewski, M.; von Grotthuss, M.; Rychlewski, L.; Ginalski, K. VoteDock: Consensus docking method for prediction of protein-ligand interactions. J. Comput. Chem., 2011, 32(4), 568-581.
[http://dx.doi.org/10.1002/jcc.21642] [PMID: 20812324]
[91]
Shin, W.H.; Seok, C. GalaxyDock: Protein-ligand docking with flexible protein side-chains. J. Chem. Inf. Model., 2012, 52(12), 3225-3232.
[http://dx.doi.org/10.1021/ci300342z] [PMID: 23198780]
[92]
Shin, W.H.; Lee, G.R.; Seok, C. Evaluation of galaxydock based on the community structure-activity resource 2013 and 2014 benchmark studies. J. Chem. Inf. Model., 2016, 56(6), 988-995.
[http://dx.doi.org/10.1021/acs.jcim.5b00309] [PMID: 26583962]
[93]
Baek, M.; Shin, W.H.; Chung, H.W.; Seok, C. GalaxyDock BP2 score: A hybrid scoring function for accurate protein-ligand docking. J. Comput. Aided Mol. Des., 2017, 31(7), 653-666.
[http://dx.doi.org/10.1007/s10822-017-0030-9] [PMID: 28623486]
[94]
Zhang, N.; Zhao, H. Enriching screening libraries with bioactive fragment space. Bioorg. Med. Chem. Lett., 2016, 26(15), 3594-3597.
[http://dx.doi.org/10.1016/j.bmcl.2016.06.013] [PMID: 27311891]
[95]
Huang, S.Y.; Zou, X. Construction and test of ligand decoy sets using MDock: Community structure-activity resource benchmarks for binding mode prediction. J. Chem. Inf. Model., 2011, 51(9), 2107-2114.
[http://dx.doi.org/10.1021/ci200080g] [PMID: 21755952]
[96]
Ng, M.C.K.; Fong, S.; Siu, S.W.I. PSOVina: The hybrid particle swarm optimization algorithm for protein-ligand docking. J. Bioinform. Comput. Biol., 2015, 13(3), 1541007.
[http://dx.doi.org/10.1142/S0219720015410073] [PMID: 25800162]
[97]
Alhossary, A.; Handoko, S.D.; Mu, Y.; Kwoh, C.K. Fast, accurate, and reliable molecular docking with QuickVina 2. Bioinformatics, 2015, 31(13), 2214-2216.
[http://dx.doi.org/10.1093/bioinformatics/btv082] [PMID: 25717194]
[98]
Ruiz-Carmona, S.; Alvarez-Garcia, D.; Foloppe, N.; Garmendia-Doval, A.B.; Juhos, S.; Schmidtke, P.; Barril, X.; Hubbard, R.E.; Morley, S.D. rDock: A fast, versatile and open source program for docking ligands to proteins and nucleic acids. PLOS Comput. Biol., 2014, 10(4), e1003571.
[http://dx.doi.org/10.1371/journal.pcbi.1003571] [PMID: 24722481]
[99]
Kooistra, A.J.; Vischer, H.F.; McNaught-Flores, D.; Leurs, R.; de Esch, I.J.P.; de Graaf, C. Function-specific virtual screening for GPCR ligands using a combined scoring method. Sci. Rep., 2016, 6, 28288.
[http://dx.doi.org/10.1038/srep28288] [PMID: 27339552]
[100]
Marcou, G.; Rognan, D. Optimizing fragment and scaffold docking by use of molecular interaction fingerprints. J. Chem. Inf. Model., 2007, 47(1), 195-207.
[http://dx.doi.org/10.1021/ci600342e] [PMID: 17238265]
[101]
Chaput, L.; Martinez-Sanz, J.; Quiniou, E.; Rigolet, P.; Saettel, N.; Mouawad, L. vSDC: A method to improve early recognition in virtual screening when limited experimental resources are available. J. Cheminform., 2016, 8(1), 1-18.
[http://dx.doi.org/10.1186/s13321-016-0112-z] [PMID: 26807156]
[102]
Gupta, A.; Chaudhary, N.; Kakularam, K.R.; Pallu, R.; Polamarasetty, A. The augmenting effects of desolvation and conformational energy terms on the predictions of docking programs against mPGES-1. PLoS One, 2015, 10(8), e0134472.
[http://dx.doi.org/10.1371/journal.pone.0134472] [PMID: 26305898]
[103]
Jacobsson, M.; Karlén, A. Ligand bias of scoring functions in structure-based virtual screening. J. Chem. Inf. Model., 2006, 46(3), 1334-1343.
[http://dx.doi.org/10.1021/ci050407t] [PMID: 16711752]
[104]
Dong, D.; Xu, Z.; Zhong, W.; Peng, S. Parallelization of molecular docking: A review. Curr. Top. Med. Chem., 2018, 18(12), 1015-1028.
[http://dx.doi.org/10.2174/1568026618666180821145215] [PMID: 30129415]