Docking Paradigm in Drug Design

Vladimir   B.    Sulimov; Danil   C.    Kutov; Anna   S.    Taschilova; Ivan   S.    Ilin; Eugene   E.    Tyrtyshnikov; Alexey   V.    Sulimov
doi:10.2174/1568026620666201207095626
Abstract

Docking is in demand for the rational computer aided structure based drug design. A review of docking methods and programs is presented. Different types of docking programs are described. They include docking of non-covalent small ligands, protein-protein docking, supercomputer docking, quantum docking, the new generation of docking programs and the application of docking for covalent inhibitors discovery. Taking into account the threat of COVID-19, we present here a short review of docking applications to the discovery of inhibitors of SARS-CoV and SARS-CoV-2 target proteins, including our own result of the search for inhibitors of SARS-CoV-2 main protease using docking and quantum chemical post-processing. The conclusion is made that docking is extremely important in the fight against COVID-19 during the process of development of antivirus drugs having a direct action on SARS-CoV-2 target proteins.
Keywords: Docking, Global optimization, Quantum docking, Inhibitors, CADD, SARS-CoV-2, COVID-19, Mpro.
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[1] 
Yu, W.; MacKerell, A.D. Jr Computer-Aided Drug Design Methods. Methods Mol. Biol.,  2017, 1520, 85-106.
[http://dx.doi.org/10.1007/978-1-4939-6634-9_5] [PMID:  27873247] 
[2] 
Klimovich, P.V.; Shirts, M.R.; Mobley, D.L. Guidelines for the analysis of free energy calculations. J. Comput. Aided Mol. Des.,  2015, 29(5), 397-411.
[http://dx.doi.org/10.1007/s10822-015-9840-9] [PMID:  25808134] 
[3] 
Cole, D.J.; Tirado-Rives, J.; Jorgensen, W.L. Molecular dynamics and Monte Carlo simulations for protein-ligand binding and inhibitor design. Biochim. Biophys. Acta,  2015, 1850(5), 966-971.
[http://dx.doi.org/10.1016/j.bbagen.2014.08.018] [PMID:  25196360] 
[4] 
Yuriev, E.; Holien, J.; Ramsland, P.A. Improvements, trends, and new ideas in molecular docking: 2012-2013 in review. J. Mol. Recognit.,  2015, 28(10), 581-604.
[http://dx.doi.org/10.1002/jmr.2471] [PMID:  25808539] 
[5] 
Pagadala, N.S.; Syed, K.; Tuszynski, J. Software for molecular docking: a review. Biophys. Rev.,  2017, 9(2), 91-102.
[http://dx.doi.org/10.1007/s12551-016-0247-1] [PMID:  28510083] 
[6] 
Sulimov, V.B.; Kutov, D.C.; Sulimov, A.V. Advances in docking. Curr. Med. Chem.,  2019, 26(42), 7555-7580.
[http://dx.doi.org/10.2174/0929867325666180904115000] [PMID:  30182836] 
[7] 
Berman, H.M.; Westbrook, J.; Feng, Z.; Gilliland, G.; Bhat, T.N.; Weissig, H.; Shindyalov, I.N.; Bourne, P.E. The protein data bank. Nucleic Acids Res.,  2000, 28(1), 235-242.
[http://dx.doi.org/10.1093/nar/28.1.235] [PMID:  10592235] 
[8] 
Wang, Z.; Sun, H.; Yao, X.; Li, D.; Xu, L.; Li, Y.; Tian, S.; Hou, T. Comprehensive evaluation of ten docking programs on a diverse set of protein-ligand complexes: the prediction accuracy of sampling power and scoring power. Phys. Chem. Chem. Phys.,  2016, 18(18), 12964-12975.
[http://dx.doi.org/10.1039/C6CP01555G] [PMID:  27108770] 
[9] 
Sulimov, A.V.; Kutov, D.C.; Katkova, E.V.; Kondakova, O.A.; Sulimov, V.B. Search for approaches to improving the calculation accuracy of the protein-ligand binding energy by docking. Russ. Chem. Bull.,  2017, 66, 1913-1924.
[http://dx.doi.org/10.1007/s11172-017-1966-6] 
[10] 
Kutov, D.C.; Katkova, E.V.; Kondakova, O.A.; Sulimov, A.V.; Sulimov, V.B. Influence of the method of hydrogen atoms incorporation into the target protein on the protein-ligand binding energy. Bull. South Ural State Univ. Ser. Math. Model. Program. Comput. Softw.,  2017, 10, 94-107.
[http://dx.doi.org/10.14529/mmp170308] 
[11] 
Sastry, G.M.; Adzhigirey, M.; Day, T.; Annabhimoju, R.; Sherman, W. Protein and ligand preparation: parameters, protocols, and influence on virtual screening enrichments. J. Comput. Aided Mol. Des.,  2013, 27(3), 221-234.
[http://dx.doi.org/10.1007/s10822-013-9644-8] [PMID:  23579614] 
[12] 
Brandon, C.J.; Martin, B.P.; McGee, K.J.; Stewart, J.J.; Braun-Sand, S.B. An approach to creating a more realistic working model from a protein data bank entry. J. Mol. Model.,  2015, 21(1), 3.
[http://dx.doi.org/10.1007/s00894-014-2520-1] [PMID:  25605595] 
[13] 
Oferkin, I.V.; Zheltkov, D.A.; Tyrtyshnikov, E.E.; Sulimov, A.V.; Kutov, D.C.; Sulimov, V.B. Evaluation of the docking algorithm based on tensor train global optimization. Bull. South Ural State Univ. Ser. Math. Model. Program. Comput. Softw.,  2015, 8, 83-99.
[14] 
Pettersen, E.F.; Goddard, T.D.; Huang, C.C.; Couch, G.S.; Greenblatt, D.M.; Meng, E.C.; Ferrin, T.E. UCSF Chimera--a visualization system for exploratory research and analysis. J. Comput. Chem.,  2004, 25(13), 1605-1612.
[http://dx.doi.org/10.1002/jcc.20084] [PMID:  15264254] 
[15] 
Word, J.M.; Lovell, S.C.; Richardson, J.S.; Richardson, D.C. Asparagine and glutamine: using hydrogen atom contacts in the choice of side-chain amide orientation. J. Mol. Biol.,  1999, 285, 1735-1747.
[16] 
Morris, G.M.; Huey, R.; Lindstrom, W.; Sanner, M.F.; Belew, R.K.; Goodsell, D.S.; Olson, A.J. AutoDock4 and AutoDockTools4: Automated docking with selective receptor flexibility. J. Comput. Chem.,  2009, 30(16), 2785-2791.
[http://dx.doi.org/10.1002/jcc.21256] [PMID:  19399780] 
[17] 
Stewart, J.J. Optimization of parameters for semiempirical methods VI: more modifications to the NDDO approximations and re-optimization of parameters. J. Mol. Model.,  2013, 19(1), 1-32.
[http://dx.doi.org/10.1007/s00894-012-1667-x] [PMID:  23187683] 
[18] 
Stewart, J.J.P. MOPAC. Stewart Computational Chemistry, 2016.
[19] 
Hanwell, M.D.; Curtis, D.E.; Lonie, D.C.; Vandermeersch, T.; Zurek, E.; Hutchison, G.R. Avogadro: an advanced semantic chemical editor, visualization, and analysis platform. J. Cheminform.,  2012, 4(1), 17.
[http://dx.doi.org/10.1186/1758-2946-4-17] [PMID:  22889332] 
[20] 
Halgren, T.A. Merck molecular force field. J. Comput. Chem.,  1996, 17, 490-641.
[http://dx.doi.org/10.1002/(SICI)1096-987X(199604)17:5/6<490:AID-JCC1>3.0.CO;2-P] 
[21] 
Glaab, E. Building a virtual ligand screening pipeline using free software: a survey. Brief. Bioinform.,  2016, 17(2), 352-366.
[http://dx.doi.org/10.1093/bib/bbv037] [PMID:  26094053] 
[22] 
Brooks, B.R.; Brooks, C.L., III; Mackerell, A.D., Jr; Nilsson, L.; Petrella, R.J.; Roux, B.; Won, Y.; Archontis, G.; Bartels, C.; Boresch, S.; Caflisch, A.; Caves, L.; Cui, Q.; Dinner, A.R.; Feig, M.; Fischer, S.; Gao, J.; Hodoscek, M. Im, W.; Kuczera, K.; Lazaridis, T.; Ma, J.; Ovchinnikov, V.; Paci, E.; Pastor, R.W.; Post, C.B.; Pu, J.Z.; Schaefer, M.; Tidor, B.; Venable, R.M.; Woodcock, H.L.; Wu, X.; Yang, W.; York, D.M.; Karplus, M. CHARMM: the biomolecular simulation program. J. Comput. Chem.,  2009, 30(10), 1545-1614.
[http://dx.doi.org/10.1002/jcc.21287] [PMID:  19444816] 
[23] 
Case, D.A.; Pearlman, D.A.; Caldwell, J.C.; Cheatham, T.E., III; Wang, J.; Ross, W.S.; Simmerling, C.L.; Darden, T.A.; Merz, K.M.; Stanton, R.V.; Cheng, A.; Vincent, J.J.; Crowley, M.; Tsui, V.; Gohlke, H.; Radmer, R.J.; Duan, Y.; Pitera, J.; Massova, I.; Seibel, G.L.; Singh, U.C.; Weiner, P.; Kollman, P.A. AMBER 7, 2002.
[24] 
Schrödinger, L.L.C.  Available from:
                    https://www.schrodinger.com
[25] 
Neves, M.A.C.; Totrov, M.; Abagyan, R. Docking and scoring with ICM: the benchmarking results and strategies for improvement. J. Comput. Aided Mol. Des.,  2012, 26(6), 675-686.
[http://dx.doi.org/10.1007/s10822-012-9547-0] [PMID:  22569591] 
[26] 
Molecular Operating Environment (MOE). Chemical Computing Group ULC 2017.
[27] 
FlexX Version 4.3. BioSolveIT GmbH 2007.
[28] 
Chen, Y.C. Beware of docking! Trends Pharmacol. Sci.,  2015, 36(2), 78-95.
[http://dx.doi.org/10.1016/j.tips.2014.12.001] [PMID:  25543280] 
[29] 
Forli, S.; Huey, R.; Pique, M.E.; Sanner, M.F.; Goodsell, D.S.; Olson, A.J. Computational protein-ligand docking and virtual drug screening with the AutoDock suite. Nat. Protoc.,  2016, 11(5), 905-919.
[http://dx.doi.org/10.1038/nprot.2016.051] [PMID:  27077332] 
[30] 
The Scripps Research Institute. AutoDock Suite., 2019.
[31] 
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.
[PMID:  19499576] 
[32] 
Baxter, J. Local optima avoidance in depot location. J. Oper. Res. Soc.,  1981, 32, 815-819.
[http://dx.doi.org/10.1057/jors.1981.159] 
[33] 
Blum, M.; Sampels, M., Eds.; A. B. Hybrid Metaheuristics: An Emerging Approach to Optimization; Springer: Berlin, 2008. 
[http://dx.doi.org/10.1007/978-3-540-78295-7] 
[34] 
Metropolis, N.; Rosenbluth, A.W.; Rosenbluth, M.N.; Teller, A.H.; Teller, E. Equation of state calculations by fast computing machines. J. Chem. Phys.,  1953, 21, 1087-1092.
[http://dx.doi.org/10.1063/1.1699114] 
[35] 
Huey, R.; Morris, G.M.; Olson, A.J.; Goodsell, D.S. A semiempirical free energy force field with charge-based desolvation. J. Comput. Chem.,  2007, 28(6), 1145-1152.
[http://dx.doi.org/10.1002/jcc.20634] [PMID:  17274016] 
[36] 
Mortier, W.J.; Van Genechten, K.; Gasteiger, J. Electronegativity equalization: application and parametrization. J. Am. Chem. Soc.,  1985, 107, 829-835.
[http://dx.doi.org/10.1021/ja00290a017] 
[37] 
Wesson, L.; Eisenberg, D. Atomic solvation parameters applied to molecular dynamics of proteins in solution. Protein Sci.,  1992, 1(2), 227-235.
[http://dx.doi.org/10.1002/pro.5560010204] [PMID:  1304905] 
[38] 
Morris, G.M.; Goodsell, D.S.; Halliday, R.S.; Huey, R.; Hart, W.E.; Belew, R.K.; Olson, A.J. Automated docking using a lamarckian genetic algorithm and an empirical binding free energy function. J. Comput. Chem.,  1998, 19, 1639-1662.
[http://dx.doi.org/10.1002/(SICI)1096-987X(19981115)19:14<1639:AID-JCC10>3.0.CO;2-B] 
[39] 
Goodsell, D.S.; Olson, A.J. Automated docking of substrates to proteins by simulated annealing. Proteins,  1990, 8(3), 195-202.
[http://dx.doi.org/10.1002/prot.340080302] [PMID:  2281083] 
[40] 
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:AID-JMR241>3.0.CO;2-6] [PMID:  8723313] 
[41] 
Solis, F.J.; Wets, R.J-B. Minimization by random search techniques. Math. Oper. Res.,  1981, 6, 19-30.
[http://dx.doi.org/10.1287/moor.6.1.19] 
[42] 
Ravindranath, P.A.; Forli, S.; Goodsell, D.S.; Olson, A.J.; Sanner, M.F. AutoDockFR: Advances in protein-ligand docking with explicitly specified binding site flexibility. PLOS Comput. Biol.,  2015, 11(12)e1004586
[http://dx.doi.org/10.1371/journal.pcbi.1004586] [PMID:  26629955] 
[43] 
Zhao, Y.; Stoffler, D.; Sanner, M. Hierarchical and multi-resolution representation of protein flexibility. Bioinformatics,  2006, 22(22), 2768-2774.
[http://dx.doi.org/10.1093/bioinformatics/btl481] [PMID:  16984893] 
[44] 
Zhang, Y.; Sanner, M.F. Docking flexible cyclic peptides with autodock CrankPep. J. Chem. Theory Comput.,  2019, 15(10), 5161-5168.
[http://dx.doi.org/10.1021/acs.jctc.9b00557] [PMID:  31505931] 
[45] 
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] 
[46] 
Brozell, S.R.; Mukherjee, S.; Balius, T.E.; Roe, D.R.; Case, D.A.; Rizzo, R.C. Evaluation of DOCK 6 as a pose generation and database enrichment tool. J. Comput. Aided Mol. Des.,  2012, 26(6), 749-773.
[http://dx.doi.org/10.1007/s10822-012-9565-y] [PMID:  22569593] 
[47] 
Kolossvary, I.; Guida, W.C. Low mode search. an efficient, automated computational method for conformational analysis: apprication to cyclic and acyclic alkanes and cyclic peptides. J. Am. Chem. Soc.,  1996, 118, 5011-5019.
[http://dx.doi.org/10.1021/ja952478m] 
[48] 
Kolossvary, I.; Keseru, G.M. Hessian-free low-mode conformational search for large-scale protein loop optimization: application to c-jun n-terminal kinase jnk3. J. Comput. Chem.,  2001, 22, 21-30.
[http://dx.doi.org/10.1002/1096-987X(20010115)22:1<21:AID-JCC3>3.0.CO;2-I] 
[49] 
Liebeschuetz, J.W.; Cole, J.C.; Korb, O. Pose prediction and virtual screening performance of GOLD scoring functions in a standardized test. J. Comput. Aided Mol. Des.,  2012, 26(6), 737-748.
[http://dx.doi.org/10.1007/s10822-012-9551-4] [PMID:  22371207] 
[50] 
Cole, J.C.; Nissink, J.W.M.; Taylor, R. Protein-ligand docking and virtual screening with gold. InVirtual Screening in Drug Discovery; Alvarez, J.; Shoichet, B.K., Eds.; Taylor & Francis Group: Oxfordshire, 2005, pp. 379-415.
[http://dx.doi.org/10.1201/9781420028775.ch15] 
[51] 
Verdonk, M.L.; Cole, J.C.; Hartshorn, M.J.; Murray, C.W.; Taylor, R.D. Improved protein-ligand docking using GOLD. Proteins,  2003, 52(4), 609-623.
[http://dx.doi.org/10.1002/prot.10465] [PMID:  12910460] 
[52] 
Totrov, M.; Abagyan, R. Flexible protein-ligand docking by global energy optimization in internal coordinates. Proteins,  1997(Suppl. 1), 215-220.
[http://dx.doi.org/10.1002/(SICI)1097-0134(1997)1+<215:AID-PROT29>3.0.CO;2-Q] [PMID:  9485515] 
[53] 
Abagyan, R.; Totrov, M.; Kuznetsov, D. ICM - A new method for protein modeling and design: applications to docking and structure prediction from the distorted native conformation. J. Comput. Chem.,  1994, 15, 488-506.
[http://dx.doi.org/10.1002/jcc.540150503] 
[54] 
Abagyan, R.; Totrov, M. Biased probability Monte Carlo conformational searches and electrostatic calculations for peptides and proteins. J. Mol. Biol.,  1994, 235(3), 983-1002.
[http://dx.doi.org/10.1006/jmbi.1994.1052] [PMID:  8289329] 
[55] 
Arnautova, Y.A.; Abagyan, R.A.; Totrov, M. Development of a new physics-based internal coordinate mechanics force field and its application to protein loop modeling. Proteins,  2011, 79(2), 477-498.
[http://dx.doi.org/10.1002/prot.22896] [PMID:  21069716] 
[56] 
Arnautova, Y.A.; Jagielska, A.; Scheraga, H.A. A new force field (ECEPP-05) for peptides, proteins, and organic molecules. J. Phys. Chem. B,  2006, 110(10), 5025-5044.
[http://dx.doi.org/10.1021/jp054994x] [PMID:  16526746] 
[57] 
Schapira, M.; Abagyan, R.; Totrov, M. Nuclear hormone receptor targeted virtual screening. J. Med. Chem.,  2003, 46(14), 3045-3059.
[http://dx.doi.org/10.1021/jm0300173] [PMID:  12825943] 
[58] 
Schapira, M.; Totrov, M.; Abagyan, R. Prediction of the binding energy for small molecules, peptides and proteins. J. Mol. Recognit.,  1999, 12(3), 177-190.
[http://dx.doi.org/10.1002/(SICI)1099-1352(199905/06)12:3<177:AID-JMR451>3.0.CO;2-Z] [PMID:  10398408] 
[59] 
Repasky, M.P.; Murphy, R.B.; Banks, J.L.; Greenwood, J.R.; Tubert-Brohman, I.; Bhat, S.; Friesner, R.A. Docking performance of the glide program as evaluated on the Astex and DUD datasets: a complete set of glide SP results and selected results for a new scoring function integrating WaterMap and glide. J. Comput. Aided Mol. Des.,  2012, 26(6), 787-799.
[http://dx.doi.org/10.1007/s10822-012-9575-9] [PMID:  22576241] 
[60] 
Friesner, R.A.; Murphy, R.B.; Repasky, M.P.; Frye, L.L.; Greenwood, J.R.; Halgren, T.A.; Sanschagrin, P.C.; Mainz, D.T. Extra precision glide: docking and scoring incorporating a model of hydrophobic enclosure for protein-ligand complexes. J. Med. Chem.,  2006, 49(21), 6177-6196.
[http://dx.doi.org/10.1021/jm051256o] [PMID:  17034125] 
[61] 
Tubert-Brohman, I.; Sherman, W.; Repasky, M.; Beuming, T. Improved docking of polypeptides with Glide. J. Chem. Inf. Model.,  2013, 53(7), 1689-1699.
[http://dx.doi.org/10.1021/ci400128m] [PMID:  23800267] 
[62] 
Friesner, R.A.; Banks, J.L.; Murphy, R.B.; Halgren, T.A.; Klicic, J.J.; Mainz, D.T.; Repasky, M.P.; Knoll, E.H.; Shelley, M.; Perry, J.K.; Shaw, D.E.; Francis, P.; Shenkin, P.S. Glide: a new approach for rapid, accurate docking and scoring. 1. Method and assessment of docking accuracy. J. Med. Chem.,  2004, 47(7), 1739-1749.
[http://dx.doi.org/10.1021/jm0306430] [PMID:  15027865] 
[63] 
Halgren, T.A.; Murphy, R.B.; Friesner, R.A.; Beard, H.S.; Frye, L.L.; Pollard, W.T.; Banks, J.L. Glide: a new approach for rapid, accurate docking and scoring. 2. Enrichment factors in database screening. J. Med. Chem.,  2004, 47(7), 1750-1759.
[http://dx.doi.org/10.1021/jm030644s] [PMID:  15027866] 
[64] 
Jorgensen, W.L.; Maxwell, D.S.; Tirado-Rives, J. Development and testing of the opls all-atom force field on conformational energetics and properties of organic liquids. J. Am. Chem. Soc.,  1996, 118, 11225-11236.
[http://dx.doi.org/10.1021/ja9621760] 
[65] 
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] 
[66] 
Jain, A.N. Morphological similarity: a 3D molecular similarity method correlated with protein-ligand recognition. J. Comput. Aided Mol. Des.,  2000, 14(2), 199-213.
[http://dx.doi.org/10.1023/A:1008100132405] [PMID:  10721506] 
[67] 
Jain, A.N. Scoring noncovalent protein-ligand interactions: a continuous differentiable function tuned to compute binding affinities. J. Comput. Aided Mol. Des.,  1996, 10(5), 427-440.
[http://dx.doi.org/10.1007/BF00124474] [PMID:  8951652] 
[68] 
Pham, T.A.; Jain, A.N. Customizing scoring functions for docking. J. Comput. Aided Mol. Des.,  2008, 22(5), 269-286.
[http://dx.doi.org/10.1007/s10822-008-9174-y] [PMID:  18273558] 
[69] 
Spitzer, R.; Jain, A.N. Surflex-dock: docking benchmarks and real-world application. J. Comput. Aided Mol. Des.,  2012, 26(6), 687-699.
[http://dx.doi.org/10.1007/s10822-011-9533-y] [PMID:  22569590] 
[70] 
Jain, T.; Jayaram, B. Computational protocol for predicting the binding affinities of zinc containing metalloprotein-ligand complexes. Proteins,  2007, 67(4), 1167-1178.
[http://dx.doi.org/10.1002/prot.21332] [PMID:  17380508] 
[71] 
Jain, A.N. Effects of protein conformation in docking: improved pose prediction through protein pocket adaptation. J. Comput. Aided Mol. Des.,  2009, 23(6), 355-374.
[http://dx.doi.org/10.1007/s10822-009-9266-3] [PMID:  19340588] 
[72] 
Cleves, A.E.; Jain, A.N. Knowledge-guided docking: accurate prospective prediction of bound configurations of novel ligands using Surflex-Dock. J. Comput. Aided Mol. Des.,  2015, 29(6), 485-509.
[http://dx.doi.org/10.1007/s10822-015-9846-3] [PMID:  25940276] 
[73] 
Cleves, A.E.; Jain, A.N. Structure- and ligand-based virtual screening on dud-e+: performance dependence on approximations to the binding pocket. J. Chem. Inf. Model.,  2020, 60(9), 4296-4310.
[http://dx.doi.org/10.1021/acs.jcim.0c00115] [PMID:  32271577] 
[74] 
Surflex, BioPharmics LLC 2020.
[75] 
Rarey, M.; Kramer, B.; Lengauer, T.; Klebe, G. A fast flexible docking method using an incremental construction algorithm. J. Mol. Biol.,  1996, 261(3), 470-489.
[http://dx.doi.org/10.1006/jmbi.1996.0477] [PMID:  8780787] 
[76] 
Böhm, H-J. The computer program LUDI: a new method for the de novo design of enzyme inhibitors. J. Comput. Aided Mol. Des.,  1992, 6(1), 61-78.
[http://dx.doi.org/10.1007/BF00124387] [PMID:  1583540] 
[77] 
Böhm, H-J. LUDI: rule-based automatic design of new substituents for enzyme inhibitor leads. J. Comput. Aided Mol. Des.,  1992, 6(6), 593-606.
[http://dx.doi.org/10.1007/BF00126217] [PMID:  1291628] 
[78] 
Ewing, T.; Kuntz, D.I. Critical evaluation of search algorithms for automated molecular docking and database screening. J. Comput. Chem.,  1997, 18, 1175-1189.
[http://dx.doi.org/10.1002/(SICI)1096-987X(19970715)18:9<1175:AID-JCC6>3.0.CO;2-O] 
[79] 
Klebe, G.; Mietzner, T. A fast and efficient method to generate biologically relevant conformations. J. Comput. Aided Mol. Des.,  1994, 8(5), 583-606.
[http://dx.doi.org/10.1007/BF00123667] [PMID:  7876902] 
[80] 
Rarey, M.; Wefing, S.; Lengauer, T. Placement of medium-sized molecular fragments into active sites of proteins. J. Comput. Aided Mol. Des.,  1996, 10(1), 41-54.
[http://dx.doi.org/10.1007/BF00124464] [PMID:  8786414] 
[81] 
Böhm, H.J. The development of a simple empirical scoring function to estimate the binding constant for a protein-ligand complex of known three-dimensional structure. J. Comput. Aided Mol. Des.,  1994, 8(3), 243-256.
[http://dx.doi.org/10.1007/BF00126743] [PMID:  7964925] 
[82] 
Kramer, B.; Rarey, M.; Lengauer, T. Evaluation of the FLEXX incremental construction algorithm for protein-ligand docking. Proteins,  1999, 37(2), 228-241.
[http://dx.doi.org/10.1002/(SICI)1097-0134(19991101)37:2<228:AID-PROT8>3.0.CO;2-8] [PMID:  10584068] 
[83] 
Claussen, H.; Buning, C.; Rarey, M.; Lengauer, T.; Flex, E. FlexE: efficient molecular docking considering protein structure variations. J. Mol. Biol.,  2001, 308(2), 377-395.
[http://dx.doi.org/10.1006/jmbi.2001.4551] [PMID:  11327774] 
[84] 
Kubinyi, H. Success stories of computer-aided design. InComputer Applications in Pharmaceutical Research and Development; Wiley: Hoboken, 2006, pp. 377-424.
[85] 
Zsoldos, Z.; Reid, D.; Simon, A.; Sadjad, B.S.; Johnson, A.P. eHiTS: an innovative approach to the docking and scoring function problems. Curr. Protein Pept. Sci.,  2006, 7(5), 421-435.
[http://dx.doi.org/10.2174/138920306778559412] [PMID:  17073694] 
[86] 
Zsoldos, Z.; Reid, D.; Simon, A.; Sadjad, S.B.; Johnson, A.P. eHiTS: a new fast, exhaustive flexible ligand docking system. J. Mol. Graph. Model.,  2007, 26(1), 198-212.
[http://dx.doi.org/10.1016/j.jmgm.2006.06.002] [PMID:  16860582] 
[87] 
Zhang, Y.; Lin, Y.; Zhao, H.; Guo, Q.; Yan, C.; Lin, N. Revealing the effects of the herbal pair of euphorbia kansui and glycyrrhiza on hepatocellular carcinoma ascites with integrating network target analysis and experimental validation. Int. J. Biol. Sci.,  2016, 12(5), 594-606.
[http://dx.doi.org/10.7150/ijbs.14151] [PMID:  27143956] 
[88] 
Hu, J.; Pang, W-S.; Han, J.; Zhang, K.; Zhang, J-Z.; Chen, L-D. Gualou Guizhi decoction reverses brain damage with cerebral ischemic stroke, multi-component directed multi-target to screen calcium-overload inhibitors using combination of molecular docking and protein-protein docking. J. Enzyme Inhib. Med. Chem.,  2018, 33(1), 115-125.
[http://dx.doi.org/10.1080/14756366.2017.1396457] [PMID:  29185359] 
[89] 
Hoelz, L.V.; Calil, F.A.; Nonato, M.C.; Pinheiro, L.C.; Boechat, N. Plasmodium falciparum dihydroorotate dehydrogenase: a drug target against malaria. Future Med. Chem.,  2018, 10(15), 1853-1874.
[http://dx.doi.org/10.4155/fmc-2017-0250] [PMID:  30019917] 
[90] 
Romanov, A.N.; Kondakova, O.A.; Grigoriev, F.V.; Sulimov, A.V.; Luschekina, S.V.; Martynov, Y.B.; Sulimov, V.B. The SOL docking package for computer-aided drug design. Numerical methods and programming,,  2008, 9, 213-233.
[91] 
Sulimov, A.V.; Kutov, D.C.; Oferkin, I.V.; Katkova, E.V.; Sulimov, V.B. Application of the docking program SOL for CSAR benchmark. J. Chem. Inf. Model.,  2013, 53(8), 1946-1956.
[http://dx.doi.org/10.1021/ci400094h] [PMID:  23829357] 
[92] 
Sulimov, V.B.; Ilin, I.S.; Kutov, D.C.; Sulimov, A.V. Development of docking programs for lomonosov supercomputer. J. Turkish Chem. Soc. Sect. Chem,  2020, 7, 259-276.
[93] 
Oferkin, I.V.; Sulimov, A.V.; Kondakova, O.A.; Sulimov, V.B. Implementation of parallel computing for docking programs SOLGRID and SOL.  Numerical methods and programming,  2011, 12, 9-23.
[94] 
Sinauridze, E.I.; Romanov, A.N.; Gribkova, I.V.; Kondakova, O.A.; Surov, S.S.; Gorbatenko, A.S.; Butylin, A.A.; Monakov, M.Y.; Bogolyubov, A.A.; Kuznetsov, Y.V.; Sulimov, V.B.; Ataullakhanov, F.I. New synthetic thrombin inhibitors: molecular design and experimental verification. PLoS One,  2011, 6(5)e19969
[http://dx.doi.org/10.1371/journal.pone.0019969] [PMID:  21603576] 
[95] 
Novichikhina, N.; Ilin, I.; Tashchilova, A.; Sulimov, A.; Kutov, D.; Ledenyova, I.; Krysin, M.; Shikhaliev, K.; Gantseva, A.; Gantseva, E.; Podoplelova, N.; Sulimov, V. Synthesis, docking, and in vitro anticoagulant activity assay of hybrid derivatives of pyrrolo[3,2,1-ij]quinolin-2(1H)-one as new inhibitors of factor Xa and factor XIa. Molecules,  2020, 25(8), 1889.
[http://dx.doi.org/10.3390/molecules25081889] [PMID:  32325823] 
[96] 
Fan, N.; Bauer, C.A.; Stork, C.; de Bruyn Kops, C.; Kirchmair, J. ALADDIN: Docking approach augmented by machine learning for protein structure selection yields superior virtual screening performance. Mol. Inform.,  2020, 39(4)e1900103
[http://dx.doi.org/10.1002/minf.201900103] [PMID:  31663691] 
[97] 
O’Connor, M.B.; Bennie, S.J.; Deeks, H.M.; Jamieson-Binnie, A.; Jones, A.J.; Shannon, R.J.; Walters, R.; Mitchell, T.J.; Mulholland, A.J.; Glowacki, D.R. Interactive molecular dynamics in virtual reality from quantum chemistry to drug binding: An open-source multi-person framework. J. Chem. Phys.,  2019, 150(22)220901
[http://dx.doi.org/10.1063/1.5092590] [PMID:  31202243] 
[98] 
Deeks, H.M.; Walters, R.K.; Hare, S.R.; O’Connor, M.B.; Mulholland, A.J.; Glowacki, D.R. Interactive molecular dynamics in virtual reality for accurate flexible protein-ligand docking. PLoS One,  2020, 15(3)e0228461
[http://dx.doi.org/10.1371/journal.pone.0228461] [PMID:  32160194] 
[99] 
Schaller, D.; Šribar, D.; Noonan, T.; Deng, L.; Nguyen, T.N.; Pach, S.; Machalz, D.; Bermudez, M.; Wolber, G. Next generation 3D pharmacophore modeling. WIREs Comput. Mol. Sci., 2020.10e1468
[100] 
Carlson, H.A.; Masukawa, K.M.; Rubins, K.; Bushman, F.D.; Jorgensen, W.L.; Lins, R.D.; Briggs, J.M.; McCammon, J.A. Developing a dynamic pharmacophore model for HIV-1 integrase. J. Med. Chem.,  2000, 43(11), 2100-2114.
[http://dx.doi.org/10.1021/jm990322h] [PMID:  10841789] 
[101] 
Hu, B.; Lill, M.A. Protein pharmacophore selection using hydration-site analysis. J. Chem. Inf. Model.,  2012, 52(4), 1046-1060.
[http://dx.doi.org/10.1021/ci200620h] [PMID:  22397751] 
[102] 
Jung, S.W.; Kim, M.; Ramsey, S.; Kurtzman, T.; Cho, A.E. Water pharmacophore: Designing ligands using molecular dynamics simulations with water. Sci. Rep.,  2018, 8(1), 10400.
[http://dx.doi.org/10.1038/s41598-018-28546-z] [PMID:  29991756] 
[103] 
Schaller, D.; Pach, S.; Wolber, G. PyRod: Tracing water molecules in molecular dynamics simulations. J. Chem. Inf. Model.,  2019, 59(6), 2818-2829.
[http://dx.doi.org/10.1021/acs.jcim.9b00281] [PMID:  31117512] 
[104] 
Yu, W.; Lakkaraju, S.K.; Raman, E.P.; MacKerell, A.D., Jr Site-Identification by Ligand Competitive Saturation (SILCS) assisted pharmacophore modeling. J. Comput. Aided Mol. Des.,  2014, 28(5), 491-507.
[http://dx.doi.org/10.1007/s10822-014-9728-0] [PMID:  24610239] 
[105] 
Yu, W.; Lakkaraju, S.K.; Raman, E.P.; Fang, L.; MacKerell, A.D. Jr Pharmacophore modeling using site-identification by ligand competitive saturation (SILCS) with multiple probe molecules. J. Chem. Inf. Model.,  2015, 55(2), 407-420.
[http://dx.doi.org/10.1021/ci500691p] [PMID:  25622696] 
[106] 
Vamathevan, J.; Clark, D.; Czodrowski, P.; Dunham, I.; Ferran, E.; Lee, G.; Li, B.; Madabhushi, A.; Shah, P.; Spitzer, M.; Zhao, S. Applications of machine learning in drug discovery and development. Nat. Rev. Drug Discov.,  2019, 18(6), 463-477.
[http://dx.doi.org/10.1038/s41573-019-0024-5] [PMID:  30976107] 
[107] 
Jiménez, J.; Doerr, S.; Martínez-Rosell, G.; Rose, A.S.; De Fabritiis, G. DeepSite: protein-binding site predictor using 3D-convolutional neural networks. Bioinformatics,  2017, 33(19), 3036-3042.
[http://dx.doi.org/10.1093/bioinformatics/btx350] [PMID:  28575181] 
[108] 
Jiménez, J.; Škalič, M.; Martínez-Rosell, G.; De Fabritiis, G. KDEEP: protein-ligand absolute binding affinity prediction via 3d-convolutional neural networks. J. Chem. Inf. Model.,  2018, 58(2), 287-296.
[http://dx.doi.org/10.1021/acs.jcim.7b00650] [PMID:  29309725] 
[109] 
Skalic, M.; Varela-Rial, A.; Jiménez, J.; Martínez-Rosell, G.; De Fabritiis, G. LigVoxel: inpainting binding pockets using 3D-convolutional neural networks. Bioinformatics,  2019, 35(2), 243-250.
[http://dx.doi.org/10.1093/bioinformatics/bty583] [PMID:  29982392] 
[110] 
Skalic, M.; Jiménez, J.; Sabbadin, D.; De Fabritiis, G. Shape-based generative modeling for de novo drug design. J. Chem. Inf. Model.,  2019, 59(3), 1205-1214.
[http://dx.doi.org/10.1021/acs.jcim.8b00706] [PMID:  30762364] 
[111] 
Bitencourt-Ferreira, G.; de Azevedo, W.F. Docking with SwissDock BT. InDocking Screens for Drug Discovery; Springer: New York, 2019, pp. 189-202.
[112] 
Grosdidier, A.; Zoete, V.; Michielin, O. Fast docking using the CHARMM force field with EADock DSS. J. Comput. Chem.,  2011, 32(10), 2149-2159.
[http://dx.doi.org/10.1002/jcc.21797] [PMID:  21541955] 
[113] 
Hsu, K-C.; Chen, Y-F.; Lin, S-R.; Yang, J-M. iGEMDOCK: a graphical environment of enhancing GEMDOCK using pharmacological interactions and post-screening analysis. BMC Bioinformatics,  2011, 12(Suppl. 1), S33.
[http://dx.doi.org/10.1186/1471-2105-12-S1-S33] [PMID:  21342564] 
[114] 
Yang, J-M. Development and evaluation of a generic evolutionary method for protein-ligand docking. J. Comput. Chem.,  2004, 25(6), 843-857.
[http://dx.doi.org/10.1002/jcc.20013] [PMID:  15011256] 
[115] 
Yang, J-M.; Chen, C-C. GEMDOCK: a generic evolutionary method for molecular docking. Proteins,  2004, 55(2), 288-304.
[http://dx.doi.org/10.1002/prot.20035] [PMID:  15048822] 
[116] 
Brown, B.P.; Mendenhall, J.; Meiler, J. BCL:MolAlign: three-dimensional small molecule alignment for pharmacophore mapping. J. Chem. Inf. Model.,  2019, 59(2), 689-701.
[http://dx.doi.org/10.1021/acs.jcim.9b00020] [PMID:  30707580] 
[117] 
Fine, J.; Konc, J.; Samudrala, R.; Chopra, G. CANDOCK: Chemical atomic network-based hierarchical flexible docking algorithm using generalized statistical potentials. J. Chem. Inf. Model.,  2020, 60(3), 1509-1527.
[http://dx.doi.org/10.1021/acs.jcim.9b00686] [PMID:  32069042] 
[118] 
Furlan, V.; Konc, J.; Bren, U. Inverse molecular docking as a novel approach to study anticarcinogenic and anti-neuroinflammatory effects of curcumin. Molecules,  2018, 23(12), 3351.
[http://dx.doi.org/10.3390/molecules23123351] [PMID:  30567342] 
[119] 
Jain, T.; Jayaram, B. An all atom energy based computational protocol for predicting binding affinities of protein-ligand complexes. FEBS Lett.,  2005, 579(29), 6659-6666.
[http://dx.doi.org/10.1016/j.febslet.2005.10.031] [PMID:  16307743] 
[120] 
Soni, A.; Bhat, R.; Jayaram, B. Improving the binding affinity estimations of protein-ligand complexes using machine-learning facilitated force field method. J. Comput. Aided Mol. Des.,  2020, 34(8), 817-830.
[http://dx.doi.org/10.1007/s10822-020-00305-1] [PMID:  32185583] 
[121] 
Eisenberg, D.; McLachlan, A.D. Solvation energy in protein folding and binding. Nature,  1986, 319(6050), 199-203.
[http://dx.doi.org/10.1038/319199a0] [PMID:  3945310] 
[122] 
Doig, A.J.; Sternberg, M.J. Side-chain conformational entropy in protein folding. Protein Sci.,  1995, 4(11), 2247-2251.
[http://dx.doi.org/10.1002/pro.5560041101] [PMID:  8563620] 
[123] 
Pickett, S.D.; Sternberg, M.J.E. Empirical scale of side-chain conformational entropy in protein folding. J. Mol. Biol.,  1993, 231(3), 825-839.
[http://dx.doi.org/10.1006/jmbi.1993.1329] [PMID:  8515453] 
[124] 
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] 
[125] 
Lindorff-Larsen, K.; Piana, S.; Palmo, K.; Maragakis, P.; Klepeis, J.L.; Dror, R.O.; Shaw, D.E. Improved side-chain torsion potentials for the Amber ff99SB protein force field. Proteins,  2010, 78(8), 1950-1958.
[http://dx.doi.org/10.1002/prot.22711] [PMID:  20408171] 
[126] 
Wang, J.; Wolf, R.M.; Caldwell, J.W.; Kollman, P.A.; Case, D.A. Development and testing of a general amber force field. J. Comput. Chem.,  2004, 25(9), 1157-1174.
[http://dx.doi.org/10.1002/jcc.20035] [PMID:  15116359] 
[127] 
Jakalian, A.; Jack, D.B.; Bayly, C.I. Fast, efficient generation of high-quality atomic charges. AM1-BCC model: II. Parameterization and validation. J. Comput. Chem.,  2002, 23(16), 1623-1641.
[http://dx.doi.org/10.1002/jcc.10128] [PMID:  12395429] 
[128] 
Arora, N.; Jayaram, B. Energetics of base pairs in b-dna in solution: an appraisal of potential functions and dielectric treatments. J. Phys. Chem. B,  1998, 102, 6139-6144.
[http://dx.doi.org/10.1021/jp9813692] 
[129] 
Liu, Z.; Li, Y.; Han, L.; Li, J.; Liu, J.; Zhao, Z.; Nie, W.; Liu, Y.; Wang, R. PDB-wide collection of binding data: current status of the PDBbind database. Bioinformatics,  2015, 31(3), 405-412.
[http://dx.doi.org/10.1093/bioinformatics/btu626] [PMID:  25301850] 
[130] 
Indian Institute of Technology Bappl.  Available from:
                    http://www.scfbio-iitd.res.in/bappl+/
[131] 
Su, M.; Yang, Q.; Du, Y.; Feng, G.; Liu, Z.; Li, Y.; Wang, R. Comparative assessment of scoring functions: the casf-2016 update. J. Chem. Inf. Model.,  2019, 59(2), 895-913.
[http://dx.doi.org/10.1021/acs.jcim.8b00545] [PMID:  30481020] 
[132] 
Maia, E.H.B.; Medaglia, L.R.; da Silva, A.M.; Taranto, A.G. Molecular architect: a user-friendly workflow for virtual screening. ACS Omega,  2020, 5(12), 6628-6640.
[http://dx.doi.org/10.1021/acsomega.9b04403] [PMID:  32258898] 
[133] 
Medicinal Pharmaceutical Chemistry Laboratory.  Available from:
                    http://www.drugdiscovery.com.br/software/
[134] 
Bitencourt-Ferreira, G.; de Azevedo, W.F. SAnDReS: A computational tool for docking bt. InDocking Screens for Drug Discovery; Springer New York, 2019, pp. 51-65.
[135] 
Xavier, M.M.; Heck, G.S.; Avila, M.B.; Levin, N.M.B.; Pintro, V.O.; Carvalho, N.L.; Azevedo, W.F. SAnDReS a computational tool for statistical analysis of docking results and development of scoring functions. Comb. Chem. High Throughput Screen.,  2016, 19(10), 801-812.
[http://dx.doi.org/10.2174/1386207319666160927111347] [PMID:  27686428] 
[136] 
Bitencourt-Ferreira, G.; de Azevedo, W.F. Molegro virtual docker for docking bt. InDocking Screens for Drug Discovery; Springer: New York, NY, 2019, pp. 149-167.
[137] 
Storn, R.; Price, K. Differential evolution – a simple and efficient heuristic for global optimization over continuous spaces. J. Glob. Optim.,  1997, 11, 341-359.
[http://dx.doi.org/10.1023/A:1008202821328] 
[138] 
Nelder, J.A.; Mead, R. A simplex method for function minimization. Comput. J.,  1965, 7, 308-313.
[http://dx.doi.org/10.1093/comjnl/7.4.308] 
[139] 
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] 
[140] 
Bitencourt-Ferreira, G.; de Azevedo, W.F. Molecular docking simulations with arguslab bt. InDocking Screens for Drug Discovery; Springer: New York, NY, 2019, pp. 203-220.
[141] 
Joy, S.; Nair, P.S.; Hariharan, R.; Pillai, M.R. Detailed comparison of the protein-ligand docking efficiencies of GOLD, a commercial package and ArgusLab, a licensable freeware. In Silico Biol.,  2006, 6(6), 601-605.
[PMID:  17518767] 
[142] 
Meiler, J.; Baker, D. ROSETTALIGAND: protein-small molecule docking with full side-chain flexibility. Proteins,  2006, 65(3), 538-548.
[http://dx.doi.org/10.1002/prot.21086] [PMID:  16972285] 
[143] 
Davis, I.W.; Baker, D. RosettaLigand docking with full ligand and receptor flexibility. J. Mol. Biol.,  2009, 385(2), 381-392.
[http://dx.doi.org/10.1016/j.jmb.2008.11.010] [PMID:  19041878] 
[144] 
Gray, J.J.; Moughon, S.; Wang, C.; Schueler-Furman, O.; Kuhlman, B.; Rohl, C.A.; Baker, D. Protein-protein docking with simultaneous optimization of rigid-body displacement and side-chain conformations. J. Mol. Biol.,  2003, 331(1), 281-299.
[http://dx.doi.org/10.1016/S0022-2836(03)00670-3] [PMID:  12875852] 
[145] 
Bonneau, R.; Tsai, J.; Ruczinski, I.; Chivian, D.; Rohl, C.; Strauss, C.E.M.; Baker, D. Rosetta in CASP4: progress in ab initio protein structure prediction. Proteins,  2001, 45(Suppl. 5), 119-126.
[http://dx.doi.org/10.1002/prot.1170] [PMID:  11835488] 
[146] 
Simons, K.T.; Kooperberg, C.; Huang, E.; Baker, D. Assembly of protein tertiary structures from fragments with similar local sequences using simulated annealing and Bayesian scoring functions. J. Mol. Biol.,  1997, 268(1), 209-225.
[http://dx.doi.org/10.1006/jmbi.1997.0959] [PMID:  9149153] 
[147] 
Kuhlman, B.; Baker, D. Native protein sequences are close to optimal for their structures. Proc. Natl. Acad. Sci.,  2000, 97, 10383-10388.
[http://dx.doi.org/10.1073/pnas.97.19.10383] 
[148] 
Lazaridis, T.; Karplus, M. Effective energy function for proteins in solution. Proteins,  1999, 35(2), 133-152.
[http://dx.doi.org/10.1002/(SICI)1097-0134(19990501)35:2<133:AID-PROT1>3.0.CO;2-N] [PMID:  10223287] 
[149] 
Kortemme, T.; Morozov, A.V.; Baker, D. An orientation-dependent hydrogen bonding potential improves prediction of specificity and structure for proteins and protein-protein complexes. J. Mol. Biol.,  2003, 326(4), 1239-1259.
[http://dx.doi.org/10.1016/S0022-2836(03)00021-4] [PMID:  12589766] 
[150] 
Koehl, P.; Delarue, M. Polar and nonpolar atomic environments in the protein core: implications for folding and binding. Proteins,  1994, 20(3), 264-278.
[http://dx.doi.org/10.1002/prot.340200307] [PMID:  7892175] 
[151] 
Wang, C.; Schueler-Furman, O.; Baker, D. Improved side-chain modeling for protein-protein docking. Protein Sci.,  2005, 14(5), 1328-1339.
[http://dx.doi.org/10.1110/ps.041222905] [PMID:  15802647] 
[152] 
da Silveira, N.J.F.; Pereira, F.S.S.; Elias, T.C.; Henrique, T. Web Services for Molecular Docking Simulations BT. InDocking Screens for Drug Discovery; Springer: New York, NY, 2019, pp. 221-229.
[153] 
Wang, J.; Dokholyan, N.V. MedusaDock 2.0: Efficient and accurate protein-ligand docking with constraints. J. Chem. Inf. Model.,  2019, 59(6), 2509-2515.
[http://dx.doi.org/10.1021/acs.jcim.8b00905] [PMID:  30946779] 
[154] 
Teodoro, M.L.; Kavraki, L.E. Conformational flexibility models for the receptor in structure based drug design. Curr. Pharm. Des.,  2003, 9(20), 1635-1648.
[http://dx.doi.org/10.2174/1381612033454595] [PMID:  12871062] 
[155] 
Vogt, A.D.; Di Cera, E. Conformational selection or induced fit? A critical appraisal of the kinetic mechanism. Biochemistry,  2012, 51(30), 5894-5902.
[http://dx.doi.org/10.1021/bi3006913] [PMID:  22775458] 
[156] 
Csermely, P.; Palotai, R.; Nussinov, R. Induced fit, conformational selection and independent dynamic segments: an extended view of binding events. Trends Biochem. Sci.,  2010, 35(10), 539-546.
[http://dx.doi.org/10.1016/j.tibs.2010.04.009] [PMID:  20541943] 
[157] 
Totrov, M.; Abagyan, R. Flexible ligand docking to multiple receptor conformations: a practical alternative. Curr. Opin. Struct. Biol.,  2008, 18(2), 178-184.
[http://dx.doi.org/10.1016/j.sbi.2008.01.004] [PMID:  18302984] 
[158] 
Osterberg, F.; Morris, G.M.; Sanner, M.F.; Olson, A.J.; Goodsell, D.S. Automated docking to multiple target structures: incorporation of protein mobility and structural water heterogeneity in AutoDock. Proteins,  2002, 46(1), 34-40.
[http://dx.doi.org/10.1002/prot.10028] [PMID:  11746701] 
[159] 
Zhao, Y.; Sanner, M.F. FLIPDock: docking flexible ligands into flexible receptors. Proteins,  2007, 68(3), 726-737.
[http://dx.doi.org/10.1002/prot.21423] [PMID:  17523154] 
[160] 
Ferrari, A.M.; Wei, B.Q.; Costantino, L.; Shoichet, B.K. Soft docking and multiple receptor conformations in virtual screening. J. Med. Chem.,  2004, 47(21), 5076-5084.
[http://dx.doi.org/10.1021/jm049756p] [PMID:  15456251] 
[161] 
Cavasotto, C.N.; Kovacs, J.A.; Abagyan, R.A. Representing receptor flexibility in ligand docking through relevant normal modes. J. Am. Chem. Soc.,  2005, 127(26), 9632-9640.
[http://dx.doi.org/10.1021/ja042260c] [PMID:  15984891] 
[162] 
Zavodszky, M.I.; Lei, M.; Thorpe, M.F.; Day, A.R.; Kuhn, L.A. Modeling correlated main-chain motions in proteins for flexible molecular recognition. Proteins,  2004, 57(2), 243-261.
[http://dx.doi.org/10.1002/prot.20179] [PMID:  15340912] 
[163] 
Zavodszky, M.I.; Kuhn, L.A. Side-chain flexibility in protein-ligand binding: the minimal rotation hypothesis. Protein Sci.,  2005, 14(4), 1104-1114.
[http://dx.doi.org/10.1110/ps.041153605] [PMID:  15772311] 
[164] 
Cozzini, P.; Kellogg, G.E.; Spyrakis, F.; Abraham, D.J.; Costantino, G.; Emerson, A.; Fanelli, F.; Gohlke, H.; Kuhn, L.A.; Morris, G.M.; Orozco, M.; Pertinhez, T.A.; Rizzi, M.; Sotriffer, C.A. Target flexibility: an emerging consideration in drug discovery and design. J. Med. Chem.,  2008, 51(20), 6237-6255.
[http://dx.doi.org/10.1021/jm800562d] [PMID:  18785728] 
[165] 
Damm, K.L.; Carlson, H.A. Exploring experimental sources of multiple protein conformations in structure-based drug design. J. Am. Chem. Soc.,  2007, 129(26), 8225-8235.
[http://dx.doi.org/10.1021/ja0709728] [PMID:  17555316] 
[166] 
Frembgen-Kesner, T.; Elcock, A.H. Computational sampling of a cryptic drug binding site in a protein receptor: explicit solvent molecular dynamics and inhibitor docking to p38 MAP kinase. J. Mol. Biol.,  2006, 359(1), 202-214.
[http://dx.doi.org/10.1016/j.jmb.2006.03.021] [PMID:  16616932] 
[167] 
Nichols, S.E.; Baron, R.; Ivetac, A.; McCammon, J.A. Predictive power of molecular dynamics receptor structures in virtual screening. J. Chem. Inf. Model.,  2011, 51(6), 1439-1446.
[http://dx.doi.org/10.1021/ci200117n] [PMID:  21534609] 
[168] 
Alberts, I.L.; Todorov, N.P.; Dean, P.M. Receptor flexibility in de novo ligand design and docking. J. Med. Chem.,  2005, 48(21), 6585-6596.
[http://dx.doi.org/10.1021/jm050196j] [PMID:  16220975] 
[169] 
Sherman, W.; Day, T.; Jacobson, M.P.; Friesner, R.A.; Farid, R. Novel procedure for modeling ligand/receptor induced fit effects. J. Med. Chem.,  2006, 49(2), 534-553.
[http://dx.doi.org/10.1021/jm050540c] [PMID:  16420040] 
[170] 
Wei, B.Q.; Weaver, L.H.; Ferrari, A.M.; Matthews, B.W.; Shoichet, B.K. Testing a flexible-receptor docking algorithm in a model binding site. J. Mol. Biol.,  2004, 337(5), 1161-1182.
[http://dx.doi.org/10.1016/j.jmb.2004.02.015] [PMID:  15046985] 
[171] 
Corbeil, C.R.; Englebienne, P.; Moitessier, N. Docking ligands into flexible and solvated macromolecules. 1. Development and validation of FITTED 1.0. J. Chem. Inf. Model.,  2007, 47(2), 435-449.
[http://dx.doi.org/10.1021/ci6002637] [PMID:  17305329] 
[172] 
Lexa, K.W.; Carlson, H.A. Protein flexibility in docking and surface mapping. Q. Rev. Biophys.,  2012, 45(3), 301-343.
[http://dx.doi.org/10.1017/S0033583512000066] [PMID:  22569329] 
[173] 
Xu, W.; Lucke, A.J.; Fairlie, D.P. Comparing sixteen scoring functions for predicting biological activities of ligands for protein targets. J. Mol. Graph. Model.,  2015, 57, 76-88.
[http://dx.doi.org/10.1016/j.jmgm.2015.01.009] [PMID:  25682361] 
[174] 
Perola, E.; Walters, W.P.; Charifson, P.S. A detailed comparison of current docking and scoring methods on systems of pharmaceutical relevance. Proteins,  2004, 56(2), 235-249.
[http://dx.doi.org/10.1002/prot.20088] [PMID:  15211508] 
[175] 
Cross, J.B.; Thompson, D.C.; Rai, B.K.; Baber, J.C.; Fan, K.Y.; Hu, Y.; Humblet, C. Comparison of several molecular docking programs: pose prediction and virtual screening accuracy. J. Chem. Inf. Model.,  2009, 49(6), 1455-1474.
[http://dx.doi.org/10.1021/ci900056c] [PMID:  19476350] 
[176] 
Li, X.; Li, Y.; Cheng, T.; Liu, Z.; Wang, R. Evaluation of the performance of four molecular docking programs on a diverse set of protein-ligand complexes. J. Comput. Chem.,  2010, 31(11), 2109-2125.
[http://dx.doi.org/10.1002/jcc.21498] [PMID:  20127741] 
[177] 
Huang, S-Y.; Grinter, S.Z.; Zou, X. Scoring functions and their evaluation methods for protein-ligand docking: recent advances and future directions. Phys. Chem. Chem. Phys.,  2010, 12(40), 12899-12908.
[http://dx.doi.org/10.1039/c0cp00151a] [PMID:  20730182] 
[178] 
Damm-Ganamet, K.L.; Smith, R.D.; Dunbar, J.B., Jr; Stuckey, J.A.; Carlson, H.A. CSAR benchmark exercise 2011-2012: evaluation of results from docking and relative ranking of blinded congeneric series. J. Chem. Inf. Model.,  2013, 53(8), 1853-1870.
[http://dx.doi.org/10.1021/ci400025f] [PMID:  23548044] 
[179] 
Sotriffer, C.A.; Gohlke, H.; Klebe, G. Docking into knowledge-based potential fields: a comparative evaluation of DrugScore. J. Med. Chem.,  2002, 45(10), 1967-1970.
[http://dx.doi.org/10.1021/jm025507u] [PMID:  11985464] 
[180] 
Liu, J.; Su, M.; Liu, Z.; Li, J.; Li, Y.; Wang, R. Enhance the performance of current scoring functions with the aid of 3D protein-ligand interaction fingerprints. BMC Bioinformatics,  2017, 18(1), 343.
[http://dx.doi.org/10.1186/s12859-017-1750-5] [PMID:  28720122] 
[181] 
Yang, J-M.; Chen, Y-F.; Shen, T-W.; Kristal, B.S.; Hsu, D.F. Consensus scoring criteria for improving enrichment in virtual screening. J. Chem. Inf. Model.,  2005, 45(4), 1134-1146.
[http://dx.doi.org/10.1021/ci050034w] [PMID:  16045308] 
[182] 
Teramoto, R.; Fukunishi, H. Supervised consensus scoring for docking and virtual screening. J. Chem. Inf. Model.,  2007, 47(2), 526-534.
[http://dx.doi.org/10.1021/ci6004993] [PMID:  17295466] 
[183] 
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] 
[184] 
Delahunty, C.M.; Yates, J.R. III Protein–Protein Interactions. InProteomics for Biological Discovery; John Wiley & Sons, Ltd: Hoboken, 2019, pp. 125-144.
[http://dx.doi.org/10.1002/9781119081661.ch5] 
[185] 
Huang, S-Y. Search strategies and evaluation in protein-protein docking: principles, advances and challenges. Drug Discov. Today,  2014, 19(8), 1081-1096.
[http://dx.doi.org/10.1016/j.drudis.2014.02.005] [PMID:  24594385] 
[186] 
Bogan, A.A.; Thorn, K.S. Anatomy of hot spots in protein interfaces. J. Mol. Biol.,  1998, 280(1), 1-9.
[http://dx.doi.org/10.1006/jmbi.1998.1843] [PMID:  9653027] 
[187] 
Clackson, T.; Wells, J.A. A hot spot of binding energy in a hormone-receptor interface. Science,  1995, 383-386.
[188] 
Moal, I.H.; Moretti, R.; Baker, D.; Fernández-Recio, J. Scoring functions for protein-protein interactions. Curr. Opin. Struct. Biol.,  2013, 23(6), 862-867.
[http://dx.doi.org/10.1016/j.sbi.2013.06.017] [PMID:  23871100] 
[189] 
Moal, I.H.; Torchala, M.; Bates, P.A.; Fernández-Recio, J. The scoring of poses in protein-protein docking: current capabilities and future directions. BMC Bioinformatics,  2013, 14, 286.
[http://dx.doi.org/10.1186/1471-2105-14-286] [PMID:  24079540] 
[190] 
Andrusier, N.; Mashiach, E.; Nussinov, R.; Wolfson, H.J. Principles of flexible protein-protein docking. Proteins,  2008, 73(2), 271-289.
[http://dx.doi.org/10.1002/prot.22170] [PMID:  18655061] 
[191] 
Yan, Y.; Huang, S-Y. Pushing the accuracy limit of shape complementarity for protein-protein docking. BMC Bioinformatics,  2019, 20(Suppl. 25), 696.
[http://dx.doi.org/10.1186/s12859-019-3270-y] [PMID:  31874620] 
[192] 
Katchalski-Katzir, E.; Shariv, I.; Eisenstein, M.; Friesem, A.A.; Aflalo, C.; Vakser, I.A. Molecular surface recognition: determination of geometric fit between proteins and their ligands by correlation techniques. Proc. Natl. Acad. Sci.,  1992, 89, 2195-2199.
[193] 
Vakser, I.A. Evaluation of GRAMM low-resolution docking methodology on the hemagglutinin-antibody complex. Proteins,  1997, 29(Suppl. 1), 226-230.
[http://dx.doi.org/10.1002/(SICI)1097-0134(1997)1+<226:AID-PROT31>3.0.CO;2-O] [PMID:  9485517] 
[194] 
Mandell, J.G.; Roberts, V.A.; Pique, M.E.; Kotlovyi, V.; Mitchell, J.C.; Nelson, E.; Tsigelny, I.; Ten Eyck, L.F. Protein docking using continuum electrostatics and geometric fit. Protein Eng.,  2001, 14(2), 105-113.
[http://dx.doi.org/10.1093/protein/14.2.105] [PMID:  11297668] 
[195] 
Roberts, V.A.; Thompson, E.E.; Pique, M.E.; Perez, M.S.; Ten Eyck, L.F. DOT2: Macromolecular docking with improved biophysical models. J. Comput. Chem.,  2013, 34(20), 1743-1758.
[http://dx.doi.org/10.1002/jcc.23304] [PMID:  23695987] 
[196] 
Chen, R.; Li, L.; Weng, Z. ZDOCK: an initial-stage protein-docking algorithm. Proteins,  2003, 52(1), 80-87.
[http://dx.doi.org/10.1002/prot.10389] [PMID:  12784371] 
[197] 
Heifetz, A.; Katchalski-Katzir, E.; Eisenstein, M. Electrostatics in protein-protein docking. Protein Sci.,  2002, 11(3), 571-587.
[http://dx.doi.org/10.1110/ps.26002] [PMID:  11847280] 
[198] 
Kozakov, D.; Brenke, R.; Comeau, S.R.; Vajda, S. PIPER: an FFT-based protein docking program with pairwise potentials. Proteins,  2006, 65(2), 392-406.
[http://dx.doi.org/10.1002/prot.21117] [PMID:  16933295] 
[199] 
Yan, Y.; He, J.; Feng, Y.; Lin, P.; Tao, H.; Huang, S-Y. Challenges and opportunities of automated protein-protein docking: hdock server vs human predictions in capri rounds 38-46. Proteins Struct. Funct. Bioinforma.,  2020, 88(8), 1055-1069.
[200] 
HDOCK, Server Lab of Bioinformatics and Molecular Modeling. 2020.
[201] 
Jiménez-García, B.; Roel-Touris, J.; Romero-Durana, M.; Vidal, M.; Jiménez-González, D.; Fernández-Recio, J. LightDock: a new multi-scale approach to protein-protein docking. Bioinformatics,  2018, 34(1), 49-55.
[http://dx.doi.org/10.1093/bioinformatics/btx555] [PMID:  28968719] 
[202] 
Lensink, M.F.; Velankar, S.; Kryshtafovych, A.; Huang, S-Y.; Schneidman-Duhovny, D.; Sali, A.; Segura, J.; Fernandez-Fuentes, N.; Viswanath, S.; Elber, R.; Grudinin, S.; Popov, P.; Neveu, E.; Lee, H.; Baek, M.; Park, S.; Heo, L.; Rie Lee, G.; Seok, C.; Qin, S.; Zhou, H-X.; Ritchie, D.W.; Maigret, B.; Devignes, M-D.; Ghoorah, A.; Torchala, M.; Chaleil, R.A.G.; Bates, P.A.; Ben-Zeev, E.; Eisenstein, M.; Negi, S.S.; Weng, Z.; Vreven, T.; Pierce, B.G.; Borrman, T.M.; Yu, J.; Ochsenbein, F.; Guerois, R.; Vangone, A.; Rodrigues, J.P.G.L.M.; van Zundert, G.; Nellen, M.; Xue, L.; Karaca, E.; Melquiond, A.S.J.; Visscher, K.; Kastritis, P.L.; Bonvin, A.M.J.J.; Xu, X.; Qiu, L.; Yan, C.; Li, J.; Ma, Z.; Cheng, J.; Zou, X.; Shen, Y.; Peterson, L.X.; Kim, H-R.; Roy, A.; Han, X.; Esquivel-Rodriguez, J.; Kihara, D.; Yu, X.; Bruce, N.J.; Fuller, J.C.; Wade, R.C.; Anishchenko, I.; Kundrotas, P.J.; Vakser, I.A.; Imai, K.; Yamada, K.; Oda, T.; Nakamura, T.; Tomii, K.; Pallara, C.; Romero-Durana, M.; Jiménez-García, B.; Moal, I.H.; Férnandez-Recio, J.; Joung, J.Y.; Kim, J.Y.; Joo, K.; Lee, J.; Kozakov, D.; Vajda, S.; Mottarella, S.; Hall, D.R.; Beglov, D.; Mamonov, A.; Xia, B.; Bohnuud, T.; Del Carpio, C.A.; Ichiishi, E.; Marze, N.; Kuroda, D.; Roy Burman, S.S.; Gray, J.J.; Chermak, E.; Cavallo, L.; Oliva, R.; Tovchigrechko, A.; Wodak, S.J. Prediction of homoprotein and heteroprotein complexes by protein docking and template-based modeling: A CASP-CAPRI experiment. Proteins,  2016, 84(Suppl. 1), 323-348.
[http://dx.doi.org/10.1002/prot.25007] [PMID:  27122118] 
[203] 
Lensink, M.F.; Velankar, S.; Wodak, S.J.  Modeling protein– protein and protein–peptide complexes: CAPRI 6th Edition. In Proteins,  2017, 85(3), 359-377.
[204] 
Lensink, M.F.; Nadzirin, N.; Velankar, S.; Wodak, S.J. Modeling protein-protein, protein-peptide, and protein-oligosaccharide complexes: CAPRI 7th Edition Proteins,  2020, 88(8), 916-938.
[205] 
Fernández-Recio, J.; Totrov, M.; Abagyan, R. ICM-DISCO docking by global energy optimization with fully flexible side-chains. Proteins,  2003, 52(1), 113-117.
[http://dx.doi.org/10.1002/prot.10383] [PMID:  12784376] 
[206] 
Bastard, K.; Prévost, C.; Zacharias, M. Accounting for loop flexibility during protein-protein docking. Proteins,  2006, 62(4), 956-969.
[http://dx.doi.org/10.1002/prot.20770] [PMID:  16372349] 
[207] 
Zacharias, M. ATTRACT: protein-protein docking in CAPRI using a reduced protein model. Proteins,  2005, 60(2), 252-256.
[http://dx.doi.org/10.1002/prot.20566] [PMID:  15981270] 
[208] 
Dominguez, C.; Boelens, R.; Bonvin, A.M.J.J. HADDOCK: a protein-protein docking approach based on biochemical or biophysical information. J. Am. Chem. Soc.,  2003, 125(7), 1731-1737.
[http://dx.doi.org/10.1021/ja026939x] [PMID:  12580598] 
[209] 
de Vries, S.J.; van Dijk, A.D.J.; Krzeminski, M.; van Dijk, M.; Thureau, A.; Hsu, V.; Wassenaar, T.; Bonvin, A.M.J.J. HADDOCK versus HADDOCK: new features and performance of HADDOCK2.0 on the CAPRI targets. Proteins,  2007, 69(4), 726-733.
[http://dx.doi.org/10.1002/prot.21723] [PMID:  17803234] 
[210] 
Moal, I.H.; Bates, P.A. SwarmDock and the use of normal modes in protein-protein docking. Int. J. Mol. Sci.,  2010, 11(10), 3623-3648.
[http://dx.doi.org/10.3390/ijms11103623] [PMID:  21152290] 
[211] 
Petsalaki, E.; Russell, R.B. Peptide-mediated interactions in biological systems: new discoveries and applications. Curr. Opin. Biotechnol.,  2008, 19(4), 344-350.
[http://dx.doi.org/10.1016/j.copbio.2008.06.004] [PMID:  18602004] 
[212] 
Huang, S-Y.; Zou, X. Ensemble docking of multiple protein structures: considering protein structural variations in molecular docking. Proteins,  2007, 66(2), 399-421.
[http://dx.doi.org/10.1002/prot.21214] [PMID:  17096427] 
[213] 
Yan, Y.; Zhang, D.; Huang, S-Y. Efficient conformational ensemble generation of protein-bound peptides. J. Cheminform.,  2017, 9(1), 59.
[http://dx.doi.org/10.1186/s13321-017-0246-7] [PMID:  29168051] 
[214] 
Chakravarty, D.; McElfresh, G.W.; Kundrotas, P.J.; Vakser, I.A. How to choose templates for modeling of protein complexes: insights from benchmarking template-based docking. Proteins Struct. Funct. Bioinforma,  2020, 88(8), 1070-1081.
[215] 
Vakser, I.A. Protein-protein docking: from interaction to interactome. Biophys. J.,  2014, 107(8), 1785-1793.
[http://dx.doi.org/10.1016/j.bpj.2014.08.033] [PMID:  25418159] 
[216] 
Joint European Disruptive Initiative GrandChallenge.. 2020.
[217] 
Sulimov, A.; Kutov, D.; Zheltkov, D.; Sulimov, V. Supercomputer docking. Supercomput. Front. Innov.,  2019, 6, 26-50.
[218] 
Trager, R.E.; Giblock, P.; Soltani, S.; Upadhyay, A.A.; Rekapalli, B.; Peterson, Y.K. Docking optimization, variance and promiscuity for large-scale drug-like chemical space using high performance computing architectures. Drug Discov. Today,  2016, 21(10), 1672-1680.
[http://dx.doi.org/10.1016/j.drudis.2016.06.023] [PMID:  27352630] 
[219] 
McIntosh-Smith, S.; Price, J.; Sessions, R.B.; Ibarra, A.A. High performance in silico virtual drug screening on many-core processors. Int. J. High Perform. Comput. Appl.,  2015, 29(2), 119-134.
[http://dx.doi.org/10.1177/1094342014528252] [PMID:  25972727] 
[220] 
Gibbs, N.; Clarke, A.R.; Sessions, R.B. Ab initio protein structure prediction using physicochemical potentials and a simplified off-lattice model. Proteins,  2001, 43(2), 186-202.
[http://dx.doi.org/10.1002/1097-0134(20010501)43:2<186:AID-PROT1030>3.0.CO;2-L] [PMID:  11276088] 
[221] 
Zhang, X.; Wong, S.E.; Lightstone, F.C. Message passing interface and multithreading hybrid for parallel molecular docking of large databases on petascale high performance computing machines. J. Comput. Chem.,  2013, 34(11), 915-927.
[http://dx.doi.org/10.1002/jcc.23214] [PMID:  23345155] 
[222] 
Gorgulla, C.; Boeszoermenyi, A.; Wang, Z-F.; Fischer, P.D.; Coote, P.W.; Padmanabha Das, K.M.; Malets, Y.S.; Radchenko, D.S.; Moroz, Y.S.; Scott, D.A.; Fackeldey, K.; Hoffmann, M.; Iavniuk, I.; Wagner, G.; Arthanari, H. An open-source drug discovery platform enables ultra-large virtual screens. Nature,  2020, 580(7805), 663-668.
[http://dx.doi.org/10.1038/s41586-020-2117-z] [PMID:  32152607] 
[223] 
Slurm; Sched MD: Utah, USA. 2020.
[224] 
Open Grid Scheduler. Sun Microsystems; California, USA, 2009. 
[225] 
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] 
[226] 
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] 
[227] 
Hassan, N.M.; Alhossary, A.A.; Mu, Y.; Kwoh, C-K. Protein-ligand blind docking using quickvina-w with inter-process spatio-temporal integration. Sci. Rep.,  2017, 7(1), 15451.
[http://dx.doi.org/10.1038/s41598-017-15571-7] [PMID:  29133831] 
[228] 
Koebel, M.R.; Schmadeke, G.; Posner, R.G.; Sirimulla, S. AutoDock VinaXB: implementation of XBSF, new empirical halogen bond scoring function, into AutoDock Vina. J. Cheminform.,  2016, 8, 27.
[http://dx.doi.org/10.1186/s13321-016-0139-1] [PMID:  27195023] 
[229] 
Nivedha, A.K.; Thieker, D.F.; Makeneni, S.; Hu, H.; Woods, R.J. Vina-Carb: Improving Glycosidic Angles during Carbohydrate Docking. J. Chem. Theory Comput.,  2016, 12(2), 892-901.
[http://dx.doi.org/10.1021/acs.jctc.5b00834] [PMID:  26744922] 
[230] 
Sterling, T.; Irwin, J.J. ZINC 15--Ligand discovery for everyone. J. Chem. Inf. Model.,  2015, 55(11), 2324-2337.
[http://dx.doi.org/10.1021/acs.jcim.5b00559] [PMID:  26479676] 
[231] 
Oferkin, I.V.; Katkova, E.V.; Sulimov, A.V.; Kutov, D.C.; Sobolev, S.I.; Voevodin, V.V.; Sulimov, V.B. Evaluation of docking target functions by the comprehensive investigation of protein-ligand energy minima. Adv. Bioinforma.,  2015, 2015126858
[232] 
Sulimov, A.V.; Kutov, D.C.; Sulimov, V.B. In: Parallel supercomputer
docking program of the new generation: finding low energy
minima spectrum. Proceedings of the 4th Russian Supercomputing
Days, Russia Voevodin, V.; Sobolev, S., Eds.; Communications
in Computer and Information Science, RuSCDays 2018;
Moscow: Russian Federation 2019, 965, pp. 314-330.
[233] 
Kutov, D.C.; Sulimov, A.V.; Sulimov, V.B. Supercomputer docking: Investigation of low energy minima of protein-ligand complexes. Supercomput. Front. Innov.,  2018, 5, 134-137.
[234] 
Sulimov, V.B.; Sulimov, A.V.; Kutov, D.C.; Ilin, I.S.  In: Development of Docking Programs for Lomonosov Supercomputer. Proceedings of the Asian Federation of Medicinal Chemistry 12th International Medicinal Chemistry Symposium 2019, AFMCAIMECS 2019, 08-11 September 2019, Istanbul, Turkey, Abstract Book, 2019, p. 77. 
[http://dx.doi.org/10.18596/jotcsa.634130] 
[235] 
Byrd, R.; Lu, P.; Nocedal, J.; Zhu, C. A Limited memory algorithm for bound constrained optimization. SIAM J. Sci. Comput.,  1995, 16, 1190-1208.
[http://dx.doi.org/10.1137/0916069] 
[236] 
Zhu, C.; Byrd, R.H.; Lu, P.; Nocedal, J. Algorithm 778: L-BFGS-B: fortran subroutines for large-scale bound-constrained optimization. acm Trans. Math. Softw.,  1997, 23, 550-560.
[http://dx.doi.org/10.1145/279232.279236] 
[237] 
Sulimov, A.V.; Kutov, D.C.; Katkova, E.V.; Sulimov, V.B. Combined docking with classical force field and quantum chemical semiempirical method pm7. Adv. Bioinformatics.,  2017, 20177167691
[238] 
Sulimov, A.V.; Kutov, D.C.; Katkova, E.V.; Ilin, I.S.; Sulimov, V.B. New generation of docking programs: Supercomputer validation of force fields and quantum-chemical methods for docking. J. Mol. Graph. Model.,  2017, 78, 139-147.
[http://dx.doi.org/10.1016/j.jmgm.2017.10.007] [PMID:  29055806] 
[239] 
Sulimov, A.V.; Zheltkov, D.A.; Oferkin, I.V.; Kutov, D.C.; Katkova, E.V.; Tyrtyshnikov, E.E.; Sulimov, V.B. In: Tensor Train
 Global Optimization: Application to Docking in the Configuration
 Space with a Large Number of Dimensions. Proceedings of the 3rd
 Russian Supercomputing Days, RuSCDays 2017; Voevodin, V.V.;
 Sobolev, S.I., Eds.; Communications in Computer and Information Science; Springer: Cham, 2017, 793, pp. 151-167 
[240] 
Sulimov, A.V.; Zheltkov, D.A.; Oferkin, I.V.; Kutov, D.C.; Katkova, E.V.; Tyrtyshnikov, E.E.; Sulimov, V.B. Evaluation of the novel algorithm of flexible ligand docking with moveable target-protein atoms. Comput. Struct. Biotechnol. J.,  2017, 15, 275-285.
[http://dx.doi.org/10.1016/j.csbj.2017.02.004] [PMID:  28377797] 
[241] 
Sulimov, A.; Kutov, D.; Ilin, I.; Zheltkov, D.; Tyrtyshnikov, E.; Sulimov, V. Supercomputer docking with a large number of degrees of freedom. SAR QSAR Environ. Res.,  2019, 30(10), 733-749.
[http://dx.doi.org/10.1080/1062936X.2019.1659412] [PMID:  31547677] 
[242] 
Oseledets, I.; Tyrtyshnikov, E. Breaking the curse of dimensionality, or how to use svd in many dimensions. SIAM J. Sci. Comput.,  2009, 31, 3744-3759.
[http://dx.doi.org/10.1137/090748330] 
[243] 
Sulimov, A.V.; Kutov, D.K.; Ilin, I.S.; Shikhaliev, K.S.; Zheltkov, D.A.; Tyrtyshnikov, E.E.; Sulimov, V.B. Docking of Oligopeptides. Russ. Chem. Bull.,  2019, 68, 1780-1786.
[http://dx.doi.org/10.1007/s11172-019-2624-y] 
[244] 
Sulimov, A.V.; Kutov, D.C.; Gribkova, A.K.; Ilin, I.S.; Tashchilova, A.S.; Sulimov, V.B.  In: Search for Approaches to Supercomputer
 Quantum-Chemical Docking. Proceedings of the 5th Russian
 Supercomputing Days, RuSCDays 2019; Voevodin, V.; Sobolev,
 S., Eds.; Communications in Computer and Information Science;
 Springer: Cham, 2019, 1129, pp. 363-378. 
[http://dx.doi.org/10.1007/978-3-030-36592-9_30] 
[245] 
Sulimov, A.V.; Kutov, D.K.; Ilin, I.S.; Sulimov, V.B.  [Docking with combined use of a force field and a quantum-chemical method Biomed. Khim., 2019, 65(2), 80-85.
[http://dx.doi.org/10.18097/PBMC20196502080] [PMID:  30950811] 
[246] 
Klamt, A.; Schuurmann, G. COSMO: A new approach to dielectric screening in solvents with explicit expressions for the screening energy and its gradient. J. Chem. Soc., Perkin Trans. 2,  1993, 799-805.
[http://dx.doi.org/10.1039/P29930000799] 
[247] 
Klamt, A. Conductor-like screening model for real solvents: a new approach to the quantitative calculation of solvation phenomena. J. Phys. Chem.,  1995, 99, 2224-2235.
[http://dx.doi.org/10.1021/j100007a062] 
[248] 
Kim, M.; Cho, A.E. Incorporating QM and solvation into docking for applications to GPCR targets. Phys. Chem. Chem. Phys.,  2016, 18(40), 28281-28289.
[http://dx.doi.org/10.1039/C6CP04742D] [PMID:  27711562] 
[249] 
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] 
[250] 
Zhang, D.; Li, H.; Wang, H.; Li, L. Docking accuracy enhanced by qm-derived protein charges. Mol. Phys.,  2016, 114, 3015-3025.
[http://dx.doi.org/10.1080/00268976.2016.1213908] 
[251] 
Zhang, D.W.; Zhang, J.Z.H. Molecular fractionation with conjugate caps for full quantum mechanical calculation of protein–molecule interaction energy. J. Chem. Phys.,  2003, 119, 3599-3605.
[http://dx.doi.org/10.1063/1.1591727] 
[252] 
Gao, A.M.; Zhang, D.W.; Zhang, J.Z.H.; Zhang, Y. An efficient linear scaling method for ab initio calculation of electron density of proteins. Chem. Phys. Lett.,  2004, 394, 293-297.
[http://dx.doi.org/10.1016/j.cplett.2004.06.137] 
[253] 
Cornell, W.D.; Cieplak, P.; Bayly, C.I.; Kollmann, P.A. Application of resp charges to calculate conformational energies, hydrogen bond energies, and free energies of solvation. J. Am. Chem. Soc.,  1993, 115, 9620-9631.
[http://dx.doi.org/10.1021/ja00074a030] 
[254] 
Bayly, C.I.; Cieplak, P.; Cornell, W.; Kollman, P.A. A well-behaved electrostatic potential based method using charge restraints for deriving atomic charges: the resp model. J. Phys. Chem.,  1993, 97, 10269-10280.
[http://dx.doi.org/10.1021/j100142a004] 
[255] 
Gasteiger, J.; Marsili, M. Iterative partial equalization of orbital electronegativity—a rapid access to atomic charges. Tetrahedron,  1980, 36, 3219-3228.
[http://dx.doi.org/10.1016/0040-4020(80)80168-2] 
[256] 
Bikadi, Z.; Hazai, E. Application of the PM6 semi-empirical method to modeling proteins enhances docking accuracy of AutoDock. J. Cheminform.,  2009, 1, 15.
[http://dx.doi.org/10.1186/1758-2946-1-15] [PMID:  20150996] 
[257] 
Ryde, U.; Söderhjelm, P. Ligand-binding affinity estimates supported by quantum-mechanical methods. Chem. Rev.,  2016, 116(9), 5520-5566.
[http://dx.doi.org/10.1021/acs.chemrev.5b00630] [PMID:  27077817] 
[258] 
Nikitina, E.; Sulimov, V.; Grigoriev, F.; Kondakova, O.; Luschekina, S. Mixed implicit/explicit solvation models in quantum mechanical calculations of binding enthalpy for protein-ligand complexes international. Int. J. Quantum Chem.,  2006, 106, 1943-1963.
[http://dx.doi.org/10.1002/qua.20943] 
[259] 
Ehrlich, S.; Göller, A.H.; Grimme, S. Towards full quantum-mechanics-based protein-ligand binding affinities. ChemPhysChem,  2017, 18(8), 898-905.
[http://dx.doi.org/10.1002/cphc.201700082] [PMID:  28133881] 
[260] 
Cavasotto, C.N.; Aucar, M.G. High-throughput docking using quantum mechanical scoring. Front Chem.,  2020, 8, 246.
[http://dx.doi.org/10.3389/fchem.2020.00246] [PMID:  32373579] 
[261] 
Kitaura, K.; Ikeo, E.; Asada, T.; Nakano, T.; Uebayasi, M. Fragment molecular orbital method: an approximate computational method for large molecules. Chem. Phys. Lett.,  1999, 313, 701-706.
[http://dx.doi.org/10.1016/S0009-2614(99)00874-X] 
[262] 
Fedorov, D. Kitaura, K. The Fragment Molecular Orbital Method Practical Applications to Large Molecular Systems; CRC Press: Boca Raton, 2019. 
[263] 
Tanaka, S.; Mochizuki, Y.; Komeiji, Y.; Okiyama, Y.; Fukuzawa, K. Electron-correlated fragment-molecular-orbital calculations for biomolecular and nano systems. Phys. Chem. Chem. Phys.,  2014, 16(22), 10310-10344.
[http://dx.doi.org/10.1039/C4CP00316K] [PMID:  24740821] 
[264] 
Fedorov, D.G.; Kitaura, K.; Li, H.; Jensen, J.H.; Gordon, M.S. The polarizable continuum model (PCM) interfaced with the fragment molecular orbital method (FMO). J. Comput. Chem.,  2006, 27(8), 976-985.
[http://dx.doi.org/10.1002/jcc.20406] [PMID:  16604514] 
[265] 
Nagata, T.; Fedorov, D.G.; Li, H.; Kitaura, K. Analytic gradient for second order Møller-Plesset perturbation theory with the polarizable continuum model based on the fragment molecular orbital method. J. Chem. Phys.,  2012, 136(20)204112
[http://dx.doi.org/10.1063/1.4714601] [PMID:  22667545] 
[266] 
Watanabe, H.; Okiyama, Y.; Nakano, T.; Tanaka, S. Incorporation of solvation effects into the fragment molecular orbital calculations with the poisson–boltzmann equation. Chem. Phys. Lett.,  2010, 500, 116-119.
[http://dx.doi.org/10.1016/j.cplett.2010.10.017] 
[267] 
Hansen, N.; van Gunsteren, W.F. Practical aspects of free-energy calculations: a review. J. Chem. Theory Comput.,  2014, 10(7), 2632-2647.
[http://dx.doi.org/10.1021/ct500161f] [PMID:  26586503] 
[268] 
Wang, L.; Wu, Y.; Deng, Y.; Kim, B.; Pierce, L.; Krilov, G.; Lupyan, D.; Robinson, S.; Dahlgren, M.K.; Greenwood, J.; Romero, D.L.; Masse, C.; Knight, J.L.; Steinbrecher, T.; Beuming, T.; Damm, W.; Harder, E.; Sherman, W.; Brewer, M.; Wester, R.; Murcko, M.; Frye, L.; Farid, R.; Lin, T.; Mobley, D.L.; Jorgensen, W.L.; Berne, B.J.; Friesner, R.A.; Abel, R. Accurate and reliable prediction of relative ligand binding potency in prospective drug discovery by way of a modern free-energy calculation protocol and force field. J. Am. Chem. Soc.,  2015, 137(7), 2695-2703.
[http://dx.doi.org/10.1021/ja512751q] [PMID:  25625324] 
[269] 
Muddana, H.S.; Gilson, M.K. Calculation of host-guest binding affinities using a quantum-mechanical energy model. J. Chem. Theory Comput.,  2012, 8(6), 2023-2033.
[http://dx.doi.org/10.1021/ct3002738] [PMID:  22737045] 
[270] 
Ucisik, M.N.; Zheng, Z.; Faver, J.C.; Merz, K.M. Bringing clarity to the prediction of protein-ligand binding free energies via “blurring”. J. Chem. Theory Comput.,  2014, 10(3), 1314-1325.
[http://dx.doi.org/10.1021/ct400995c] [PMID:  24803861] 
[271] 
Chang, C-E.; Gilson, M.K. Free energy, entropy, and induced fit in host-guest recognition: calculations with the second-generation mining minima algorithm. J. Am. Chem. Soc.,  2004, 126(40), 13156-13164.
[http://dx.doi.org/10.1021/ja047115d] [PMID:  15469315] 
[272] 
Korth, M. Third-generation hydrogen-bonding corrections for semiempirical qm methods and force fields. J. Chem. Theory Comput.,  2010, 6, 3808-3816.
[http://dx.doi.org/10.1021/ct100408b] 
[273] 
Fanfrlík, J.; Bronowska, A.K.; Rezác, J.; Prenosil, O.; Konvalinka, J.; Hobza, P. 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,  2010, 114(39), 12666-12678.
[http://dx.doi.org/10.1021/jp1032965] [PMID:  20839830] 
[274] 
Pecina, A.; Meier, R.; Fanfrlík, J.; Lepšík, M.; Řezáč, J.; Hobza, P.; Baldauf, C. The SQM/COSMO filter: reliable native pose identification based on the quantum-mechanical description of protein-ligand interactions and implicit COSMO solvation. Chem. Commun. (Camb.),  2016, 52(16), 3312-3315.
[http://dx.doi.org/10.1039/C5CC09499B] [PMID:  26821703] 
[275] 
Řezáč, J.; Hobza, P. A Halogen-Bonding Correction for the Semiempirical PM6 Method. Chem. Phys. Lett.,  2011, 506, 286-289.
[http://dx.doi.org/10.1016/j.cplett.2011.03.009] 
[276] 
Hostaš, J.; Řezáč, J.; Hobza, P. On the performance of the semiempirical quantum mechanical pm6 and pm7 methods for noncovalent interactions. Chem. Phys. Lett.,  2013, 568–569, 161-166.
[http://dx.doi.org/10.1016/j.cplett.2013.02.069] 
[277] 
Řezáč, J.; Hobza, P. Advanced corrections of hydrogen bonding and dispersion for semiempirical quantum mechanical methods. J. Chem. Theory Comput.,  2012, 8(1), 141-151.
[http://dx.doi.org/10.1021/ct200751e] [PMID:  26592877] 
[278] 
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] 
[279] 
Pecina, A.; Haldar, S.; Fanfrlík, J.; Meier, R.; Řezáč, J.; Lepšík, M.; Hobza, P. SQM/COSMO scoring function at the dftb3-d3h4 level: unique identification of native protein-ligand poses. J. Chem. Inf. Model.,  2017, 57(2), 127-132.
[http://dx.doi.org/10.1021/acs.jcim.6b00513] [PMID:  28045518] 
[280] 
Nishizawa, H.; Nishimura, Y.; Kobayashi, M.; Irle, S.; Nakai, H. Three pillars for achieving quantum mechanical molecular dynamics simulations of huge systems: Divide-and-conquer, density-functional tight-binding, and massively parallel computation. J. Comput. Chem.,  2016, 37(21), 1983-1992.
[http://dx.doi.org/10.1002/jcc.24419] [PMID:  27317328] 
[281] 
Miriyala, V.M.; Řezáč, J. Description of non-covalent interactions in SCC-DFTB methods. J. Comput. Chem.,  2017, 38(10), 688-697.
[http://dx.doi.org/10.1002/jcc.24725] [PMID:  28093777] 
[282] 
Christensen, A.S.; Kubař, T.; Cui, Q.; Elstner, M. Semiempirical quantum mechanical methods for noncovalent interactions for chemical and biochemical applications. Chem. Rev.,  2016, 116(9), 5301-5337.
[http://dx.doi.org/10.1021/acs.chemrev.5b00584] [PMID:  27074247] 
[283] 
Oferkin, I.V.; Sulimov, A.V.; Katkova, E.V.; Kutov, D.K.; Grigoriev, F.V.; Kondakova, O.A.; Sulimov, V.B. Supercomputer investigation of the protein-ligand system low-energy minima. Biomed. Khim.,  2015, 61(6), 712-716.
[http://dx.doi.org/10.18097/PBMC20156106712] [PMID:  26716742] 
[284] 
Sulimov, A.V.; Kutov, D.C.; Sulimov, V.B.  In: Quasi-Docking:
 Comparison of Different Energy Functions in Docking. Proceedings
 of the 22nd European Symposium on Quantitative Structure-
 Activity Relationships, 22nd EuroQSAR, Thessaloniki, Greece -
 September 16-20, 2018, p. 124 
[285] 
Eyrilmez, S.M.; Köprülüoğlu, C.; Řezáč, J.; Hobza, P. Impressive enrichment of semiempirical quantum mechanics-based scoring function: hsp90 protein with 4541 inhibitors and decoys. ChemPhysChem,  2019, 20(21), 2759-2766.
[http://dx.doi.org/10.1002/cphc.201900628] [PMID:  31460692] 
[286] 
Mysinger, M.M.; Carchia, M.; Irwin, J.J.; Shoichet, B.K. Directory of useful decoys, enhanced (DUD-E): better ligands and decoys for better benchmarking. J. Med. Chem.,  2012, 55(14), 6582-6594.
[http://dx.doi.org/10.1021/jm300687e] [PMID:  22716043] 
[287] 
Mooij, W.T.M.; Verdonk, M.L. General and targeted statistical potentials for protein-ligand interactions. Proteins,  2005, 61(2), 272-287.
[http://dx.doi.org/10.1002/prot.20588] [PMID:  16106379] 
[288] 
Wollacott, A.M.; Merz, K.M. Development of a Parametrized Force Field To Reproduce Semiempirical Geometries. J. Chem. Theory Comput.,  2006, 2(4), 1070-1077.
[http://dx.doi.org/10.1021/ct0600161] [PMID:  26633066] 
[289] 
Mongan, J.; Simmerling, C.; McCammon, J.A.; Case, D.A.; Onufriev, A. Generalized Born model with a simple, robust molecular volume correction. J. Chem. Theory Comput.,  2007, 3(1), 156-169.
[http://dx.doi.org/10.1021/ct600085e] [PMID:  21072141] 
[290] 
Chaskar, P.; Zoete, V.; Röhrig, U.F. Toward on-the-fly quantum mechanical/molecular mechanical (QM/MM) docking: development and benchmark of a scoring function. J. Chem. Inf. Model.,  2014, 54(11), 3137-3152.
[http://dx.doi.org/10.1021/ci5004152] [PMID:  25296988] 
[291] 
Cui, Q.; Elstner, M.; Kaxiras, E.; Frauenheim, T.; Karplus, M. A QM/MM Implementation of the Self-Consistent Charge Density Functional Tight Binding (SCC-DFTB). Method. J. Phys. Chem. B,  2001, 105, 569-585.
[http://dx.doi.org/10.1021/jp0029109] 
[292] 
Elstner, M. The SCC-DFTB Method and Its Application to Biological Systems. Theor. Chem. Acc.,  2006, 116, 316-325.
[http://dx.doi.org/10.1007/s00214-005-0066-0] 
[293] 
Grosdidier, A.; Zoete, V.; Michielin, O. SwissDock, a protein-small molecule docking web service based on EADock DSS. Nucleic Acids Res,  2011, 39(Web Server issue), W270-W277.
[http://dx.doi.org/10.1093/nar/gkr366] [PMID: 21624888] 
[294] 
Zoete, V.; Grosdidier, A.; Cuendet, M.; Michielin, O. Use of the FACTS solvation model for protein-ligand docking calculations. Application to EADock. J. Mol. Recognit.,  2010, 23(5), 457-461.
[http://dx.doi.org/10.1002/jmr.1012] [PMID:  20101644] 
[295] 
Chaskar, P.; Zoete, V.; Röhrig, U.F. On-the-Fly QM/MM Docking with Attracting Cavities. J. Chem. Inf. Model.,  2017, 57(1), 73-84.
[http://dx.doi.org/10.1021/acs.jcim.6b00406] [PMID:  27983849] 
[296] 
Zoete, V.; Schuepbach, T.; Bovigny, C.; Chaskar, P.; Daina, A.; Röhrig, U.F.; Michielin, O. Attracting cavities for docking. Replacing the rough energy landscape of the protein by a smooth attracting landscape. J. Comput. Chem.,  2016, 37(4), 437-447.
[http://dx.doi.org/10.1002/jcc.24249] [PMID:  26558715] 
[297] 
Burger, S.K.; Thompson, D.C.; Ayers, P.W. Quantum mechanics/molecular mechanics strategies for docking pose refinement: distinguishing between binders and decoys in cytochrome C peroxidase. J. Chem. Inf. Model.,  2011, 51(1), 93-101.
[http://dx.doi.org/10.1021/ci100329z] [PMID:  21133348] 
[298] 
Yilmazer, N.D.; Korth, M. Recent progress in treating protein-ligand interactions with quantum-mechanical methods. Int. J. Mol. Sci.,  2016, 17(5), 742.
[http://dx.doi.org/10.3390/ijms17050742] [PMID:  27196893] 
[299] 
Sparta, M.; Neese, F. Chemical applications carried out by local pair natural orbital based coupled-cluster methods. Chem. Soc. Rev.,  2014, 43(14), 5032-5041.
[http://dx.doi.org/10.1039/C4CS00050A] [PMID:  24676339] 
[300] 
Liakos, D.G.; Neese, F. Is it possible to obtain coupled cluster quality energies at near density functional theory cost? domain-based local pair natural orbital coupled cluster vs modern density functional theory. J. Chem. Theory Comput.,  2015, 11(9), 4054-4063.
[http://dx.doi.org/10.1021/acs.jctc.5b00359] [PMID:  26575901] 
[301] 
Kříž, K.; Řezáč, J. Benchmarking of semiempirical quantum-mechanical methods on systems relevant to computer-aided drug design. J. Chem. Inf. Model.,  2020, 60(3), 1453-1460.
[http://dx.doi.org/10.1021/acs.jcim.9b01171] [PMID:  32062970] 
[302] 
May, A.J.; Manby, F.R. An explicitly correlated second order Møller-Plesset theory using a frozen Gaussian geminal. J. Chem. Phys.,  2004, 121(10), 4479-4485.
[http://dx.doi.org/10.1063/1.1780891] [PMID:  15332877] 
[303] 
TURBOMOLE. Turbomole GmbH; Karlsruhe, Germany, 2007. 
[304] 
Neese, F. Software Update: The ORCA Program System, Version 4.0. WIREs Comput. Mol. Sci.,  2018, 8e1327
[305] 
Hostaš, J.; Řezáč, J. Accurate DFT-D3 Calculations in a small basis set. J. Chem. Theory Comput.,  2017, 13(8), 3575-3585.
[http://dx.doi.org/10.1021/acs.jctc.7b00365] [PMID:  28715628] 
[306] 
Dewar, M.J.S.; Zoebisch, E.G.; Healy, E.F.; Stewart, J.J.P. Development and use of quantum mechanical molecular models. 76. am1: a new general purpose quantum mechanical molecular model. J. Am. Chem. Soc.,  1985, 107, 3902-3909.
[http://dx.doi.org/10.1021/ja00299a024] 
[307] 
Stewart, J.J. Optimization of parameters for semiempirical methods V: modification of NDDO approximations and application to 70 elements. J. Mol. Model.,  2007, 13(12), 1173-1213.
[http://dx.doi.org/10.1007/s00894-007-0233-4] [PMID:  17828561] 
[308] 
Gaus, M.; Cui, Q.; Elstner, M. DFTB3: Extension of the self-consistent-charge density-functional tight-binding method (SCC-DFTB). J. Chem. Theory Comput.,  2012, 7(4), 931-948.
[http://dx.doi.org/10.1021/ct100684s] [PMID:  23204947] 
[309] 
Řezáč, J. Empirical self-consistent correction for the description of hydrogen bonds in DFTB3. J. Chem. Theory Comput.,  2017, 13(10), 4804-4817.
[http://dx.doi.org/10.1021/acs.jctc.7b00629] [PMID:  28949517] 
[310] 
Bannwarth, C.; Ehlert, S.; Grimme, S. GFN2-xTB-An accurate and broadly parametrized self-consistent tight-binding quantum chemical method with multipole electrostatics and density-dependent dispersion contributions. J. Chem. Theory Comput.,  2019, 15(3), 1652-1671.
[http://dx.doi.org/10.1021/acs.jctc.8b01176] [PMID:  30741547] 
[311] 
Sure, R.; Grimme, S. Corrected small basis set Hartree-Fock method for large systems. J. Comput. Chem.,  2013, 34(19), 1672-1685.
[http://dx.doi.org/10.1002/jcc.23317] [PMID:  23670872] 
[312] 
Kříž, K.; Řezáč, J. Reparametrization of the cosmo solvent model for semiempirical methods pm6 and pm7. J. Chem. Inf. Model.,  2019, 59(1), 229-235.
[http://dx.doi.org/10.1021/acs.jcim.8b00681] [PMID:  30608688] 
[313] 
Hou, G.; Zhu, X.; Cui, Q. An implicit solvent model for scc-dftb with charge-dependent radii. J. Chem. Theory Comput.,  2010, 6(8), 2303-2314.
[http://dx.doi.org/10.1021/ct1001818] [PMID:  20711513] 
[314] 
Barone, V.; Carnimeo, I.; Scalmani, G. Computational spectroscopy of large systems in solution: the dftb/pcm and td-dftb/pcm approach. J. Chem. Theory Comput.,  2013, 9(4), 2052-2071.
[http://dx.doi.org/10.1021/ct301050x] [PMID:  26583552] 
[315] 
Eckert, F.; Klamt, A. Fast Solvent Screening via Quantum Chemistry: COSMO-RS Approach. AIChE J.,  2002, 48, 369-385.
[http://dx.doi.org/10.1002/aic.690480220] 
[316] 
Schmidt, M.W.; Baldridge, K.K.; Boatz, J.A.; Elbert, S.T.; Gordon, M.S.; Jensen, J.H.; Koseki, S.; Matsunaga, N.; Nguyen, K.A.; Su, S.; Windus, T.L.; Dupuis, M.; Montgomery, J.A., Jr General atomic and molecular electronic structure system. J. Comput. Chem.,  1993, 14, 1347-1363.
[http://dx.doi.org/10.1002/jcc.540141112] 
[317] 
Grigoriev, F.V.; Golovacheva, A.Y.; Romanov, A.N.; Kondakova, O.A.; Sulimov, A.V.; Smolov, M.A.; Gottikh, M.B.; Sulimov, V.B.; Bogolyubov, A.A.; Kuznetsov, Y.V.; Dutov, M.D. Stability of hiv-1 integrase–ligand complexes: the role of coordinating bonds. Struct. Chem.,  2012, 23, 185-195.
[http://dx.doi.org/10.1007/s11224-011-9855-3] 
[318] 
Guedes, I.A.; Pereira, F.S.S.; Dardenne, L.E. Empirical scoring functions for structure-based virtual screening: applications, critical aspects, and challenges. Front. Pharmacol.,  2018, 9, 1089.
[http://dx.doi.org/10.3389/fphar.2018.01089] [PMID:  30319422] 
[319] 

Quantum Mechanics in Drug Discovery; Springer: Berlin, 2020. 
[320] 
Aucar, M.G.; Cavasotto, C.N. Molecular docking using quantum mechanical-based methods. Quantum Mechanics in Drug Discovery; Springer: Berlin, 2020. 
[http://dx.doi.org/10.1007/978-1-0716-0282-9_17] 
[321] 
Singh, J.; Petter, R.C.; Baillie, T.A.; Whitty, A. The resurgence of covalent drugs. Nat. Rev. Drug Discov.,  2011, 10(4), 307-317.
[http://dx.doi.org/10.1038/nrd3410] [PMID:  21455239] 
[322] 
Bauer, R.A. Covalent inhibitors in drug discovery: from accidental discoveries to avoided liabilities and designed therapies. Drug Discov. Today,  2015, 20(9), 1061-1073.
[http://dx.doi.org/10.1016/j.drudis.2015.05.005] [PMID:  26002380] 
[323] 
Kumalo, H.M.; Bhakat, S.; Soliman, M.E.S. Theory and applications of covalent docking in drug discovery: merits and pitfalls. Molecules,  2015, 20(2), 1984-2000.
[http://dx.doi.org/10.3390/molecules20021984] [PMID:  25633330] 
[324] 
Robertson, J.G. Mechanistic basis of enzyme-targeted drugs. Biochemistry,  2005, 44(15), 5561-5571.
[http://dx.doi.org/10.1021/bi050247e] [PMID:  15823014] 
[325] 
Ai, Y.; Yu, L.; Tan, X.; Chai, X.; Liu, S. Discovery of covalent ligands via noncovalent docking by dissecting covalent docking based on a “steric-clashes alleviating receptor (scar)” strategy. J. Chem. Inf. Model.,  2016, 56(8), 1563-1575.
[http://dx.doi.org/10.1021/acs.jcim.6b00334] [PMID:  27411028] 
[326] 
Gehringer, M.; Laufer, S.A. Emerging and re-emerging warheads for targeted covalent inhibitors: applications in medicinal chemistry and chemical biology. J. Med. Chem.,  2019, 62(12), 5673-5724.
[http://dx.doi.org/10.1021/acs.jmedchem.8b01153] [PMID:  30565923] 
[327] 
Mah, R.; Thomas, J.R.; Shafer, C.M. Drug discovery considerations in the development of covalent inhibitors. Bioorg. Med. Chem. Lett.,  2014, 24(1), 33-39.
[http://dx.doi.org/10.1016/j.bmcl.2013.10.003] [PMID:  24314671] 
[328] 
Ouyang, X.; Zhou, S.; Su, C.T.T.; Ge, Z.; Li, R.; Kwoh, C.K. CovalentDock: automated covalent docking with parameterized covalent linkage energy estimation and molecular geometry constraints. J. Comput. Chem.,  2013, 34(4), 326-336.
[http://dx.doi.org/10.1002/jcc.23136] [PMID:  23034731] 
[329] 
Morse, P.M. Diatomic molecules according to the wave mechanics. ii. vibrational levels. Phys. Rev.,  1929, 34, 57-64.
[http://dx.doi.org/10.1103/PhysRev.34.57] 
[330] 
Ouyang, X.; Zhou, S.; Ge, Z.; Li, R.; Kwoh, C.K. CovalentDock Cloud: a web server for automated covalent docking. Nucleic Acids Res.,  2013, 41(Web Server issue), W329-W332.
[http://dx.doi.org/10.1093/nar/gkt406] [PMID: 23677616] 
[331] 
Zhu, K.; Borrelli, K.W.; Greenwood, J.R.; Day, T.; Abel, R.; Farid, R.S.; Harder, E. Docking covalent inhibitors: a parameter free approach to pose prediction and scoring. J. Chem. Inf. Model.,  2014, 54(7), 1932-1940.
[http://dx.doi.org/10.1021/ci500118s] [PMID:  24916536] 
[332] 
Zhu, K.; Pincus, D.L.; Zhao, S.; Friesner, R.A. Long loop prediction using the protein local optimization program. Proteins,  2006, 65(2), 438-452.
[http://dx.doi.org/10.1002/prot.21040] [PMID:  16927380] 
[333] 
Zhu, K.; Shirts, M.R.; Friesner, R.A. Improved methods for side chain and loop predictions via the protein local optimization program: variable dielectric model for implicitly improving the treatment of polarization effects. J. Chem. Theory Comput.,  2007, 3(6), 2108-2119.
[http://dx.doi.org/10.1021/ct700166f] [PMID:  26636204] 
[334] 
Li, J.; Abel, R.; Zhu, K.; Cao, Y.; Zhao, S.; Friesner, R.A. The VSGB 2.0 model: a next generation energy model for high resolution protein structure modeling. Proteins,  2011, 79(10), 2794-2812.
[http://dx.doi.org/10.1002/prot.23106] [PMID:  21905107] 
[335] 
Toledo Warshaviak, D.; Golan, G.; Borrelli, K.W.; Zhu, K.; Kalid, O. Structure-based virtual screening approach for discovery of covalently bound ligands. J. Chem. Inf. Model.,  2014, 54(7), 1941-1950.
[http://dx.doi.org/10.1021/ci500175r] [PMID:  24932913] 
[336] 
London, N.; Miller, R.M.; Krishnan, S.; Uchida, K.; Irwin, J.J.; Eidam, O.; Gibold, L.; Cimermančič, P.; Bonnet, R.; Shoichet, B.K.; Taunton, J. Covalent docking of large libraries for the discovery of chemical probes. Nat. Chem. Biol.,  2014, 10(12), 1066-1072.
[http://dx.doi.org/10.1038/nchembio.1666] [PMID:  25344815] 
[337] 
Mysinger, M.M.; Shoichet, B.K. Rapid context-dependent ligand desolvation in molecular docking. J. Chem. Inf. Model.,  2010, 50(9), 1561-1573.
[http://dx.doi.org/10.1021/ci100214a] [PMID:  20735049] 
[338] 
Scholz, C.; Knorr, S.; Hamacher, K.; Schmidt, B. DOCKTITE-a highly versatile step-by-step workflow for covalent docking and virtual screening in the molecular operating environment. J. Chem. Inf. Model.,  2015, 55(2), 398-406.
[http://dx.doi.org/10.1021/ci500681r] [PMID:  25541749] 
[339] 
Scarpino, A.; Ferenczy, G.G.; Keserű, G.M. Comparative evaluation of covalent docking tools. J. Chem. Inf. Model.,  2018, 58(7), 1441-1458.
[http://dx.doi.org/10.1021/acs.jcim.8b00228] [PMID:  29890081] 
[340] 
Moitessier, N.; Pottel, J.; Therrien, E.; Englebienne, P.; Liu, Z.; Tomberg, A.; Corbeil, C.R. Medicinal chemistry projects requiring imaginative structure-based drug design methods. Acc. Chem. Res.,  2016, 49(9), 1646-1657.
[http://dx.doi.org/10.1021/acs.accounts.6b00185] [PMID:  27529781] 
[341] 
De Cesco, S.; Deslandes, S.; Therrien, E.; Levan, D.; Cueto, M.; Schmidt, R.; Cantin, L-D.; Mittermaier, A.; Juillerat-Jeanneret, L.; Moitessier, N. Virtual screening and computational optimization for the discovery of covalent prolyl oligopeptidase inhibitors with activity in human cells. J. Med. Chem.,  2012, 55(14), 6306-6315.
[http://dx.doi.org/10.1021/jm3002839] [PMID:  22765237] 
[342] 
Katritch, V.; Byrd, C.M.; Tseitin, V.; Dai, D.; Raush, E.; Totrov, M.; Abagyan, R.; Jordan, R.; Hruby, D.E. Discovery of small molecule inhibitors of ubiquitin-like poxvirus proteinase I7L using homology modeling and covalent docking approaches. J. Comput. Aided Mol. Des.,  2007, 21(10-11), 549-558.
[http://dx.doi.org/10.1007/s10822-007-9138-7] [PMID:  17960327] 
[343] 
Myint, S.H. Human Coronavirus Infections BT.- InThe Coronaviridae; Springer US: Boston, MA, 1995, pp. 389-401.
[344] 
Ksiazek, T.G.; Erdman, D.; Goldsmith, C.S.; Zaki, S.R.; Peret, T.; Emery, S.; Tong, S.; Urbani, C.; Comer, J.A.; Lim, W.; Rollin, P.E.; Dowell, S.F.; Ling, A-E.; Humphrey, C.D.; Shieh, W-J.; Guarner, J.; Paddock, C.D.; Rota, P.; Fields, B.; DeRisi, J.; Yang, J-Y.; Cox, N.; Hughes, J.M.; LeDuc, J.W.; Bellini, W.J.; Anderson, L.J. SARS Working Group. A novel coronavirus associated with severe acute respiratory syndrome. N. Engl. J. Med.,  2003, 348(20), 1953-1966.
[http://dx.doi.org/10.1056/NEJMoa030781] [PMID:  12690092] 
[345] 
Drosten, C.; Günther, S.; Preiser, W.; van der Werf, S.; Brodt, H-R.; Becker, S.; Rabenau, H.; Panning, M.; Kolesnikova, L.; Fouchier, R.A.M.; Berger, A.; Burguière, A-M.; Cinatl, J.; Eickmann, M.; Escriou, N.; Grywna, K.; Kramme, S.; Manuguerra, J-C.; Müller, S.; Rickerts, V.; Stürmer, M.; Vieth, S.; Klenk, H-D.; Osterhaus, A.D.M.E.; Schmitz, H.; Doerr, H.W. Identification of a novel coronavirus in patients with severe acute respiratory syndrome. N. Engl. J. Med.,  2003, 348(20), 1967-1976.
[http://dx.doi.org/10.1056/NEJMoa030747] [PMID:  12690091] 
[346] 
Zaki, A.M.; van Boheemen, S.; Bestebroer, T.M.; Osterhaus, A.D.; Fouchier, R.A. Isolation of a novel coronavirus from a man with pneumonia in Saudi Arabia. N. Engl. J. Med.,  2012, 367(19), 1814-1820.
[http://dx.doi.org/10.1056/NEJMoa1211721] [PMID:  23075143] 
[347] 
Barretto, N.; Jukneliene, D.; Ratia, K.; Chen, Z.; Mesecar, A.D.; Baker, S.C. The papain-like protease of severe acute respiratory syndrome coronavirus has deubiquitinating activity. J. Virol.,  2005, 79(24), 15189-15198.
[http://dx.doi.org/10.1128/JVI.79.24.15189-15198.2005] [PMID:  16306590] 
[348] 
Anand, K.; Yang, H.; Bartlam, M.; Rao, Z.; Hilgenfeld, R. Coronavirus main proteinase: Target for antiviral drug therapy BT - coronaviruses with special emphasis on first insights concerning SARS. Birkhäuser Basel; Schmidt, A.; Weber, O; Wolff, M.H., Ed.; Basel, 2005, pp. 173-199.
[349] 
Anand, K.; Ziebuhr, J.; Wadhwani, P.; Mesters, J.R.; Hilgenfeld, R. Coronavirus main proteinase (3CLpro) structure: basis for design of anti-SARS drugs. Science,  2003, 300(5626), 1763-1767.
[http://dx.doi.org/10.1126/science.1085658] [PMID:  12746549] 
[350] 
Yang, H.; Yang, M.; Ding, Y.; Liu, Y.; Lou, Z.; Zhou, Z.; Sun, L.; Mo, L.; Ye, S.; Pang, H.; Gao, G.F.; Anand, K.; Bartlam, M.; Hilgenfeld, R.; Rao, Z. The crystal structures of severe acute respiratory syndrome virus main protease and its complex with an inhibitor. Proc. Natl. Acad. Sci. USA,  2003, 100(23), 13190-13195.
[http://dx.doi.org/10.1073/pnas.1835675100] [PMID:  14585926] 
[351] 
Jenwitheesuk, E.; Samudrala, R. Identifying inhibitors of the SARS coronavirus proteinase. Bioorg. Med. Chem. Lett.,  2003, 13(22), 3989-3992.
[http://dx.doi.org/10.1016/j.bmcl.2003.08.066] [PMID:  14592491] 
[352] 
Kalé, L.; Skeel, R.; Bhandarkar, M.; Brunner, R.; Gursoy, A.; Krawetz, N.; Phillips, J.; Shinozaki, A.; Varadarajan, K.; Schulten, K. NAMD2: Greater scalability for parallel molecular dynamics. J. Comput. Phys.,  1999, 151, 283-312.
[http://dx.doi.org/10.1006/jcph.1999.6201] 
[353] 
Brunger, A.T. A System for X-Ray Crystallography and NMR; Yale University Press: New Haven, 1992. 
[354] 
Jenwitheesuk, E.; Samudrala, R. Improved prediction of HIV-1 protease-inhibitor binding energies by molecular dynamics simulations. BMC Struct. Biol.,  2003, 3, 2.
[http://dx.doi.org/10.1186/1472-6807-3-2] [PMID:  12675950] 
[355] 
Kuo, C-J.; Chi, Y-H.; Hsu, J.T-A.; Liang, P-H. Characterization of SARS main protease and inhibitor assay using a fluorogenic substrate. Biochem. Biophys. Res. Commun.,  2004, 318(4), 862-867.
[http://dx.doi.org/10.1016/j.bbrc.2004.04.098] [PMID:  15147951] 
[356] 
Grum-Tokars, V.; Ratia, K.; Begaye, A.; Baker, S.C.; Mesecar, A.D. Evaluating the 3C-like protease activity of SARS-Coronavirus: recommendations for standardized assays for drug discovery. Virus Res.,  2008, 133(1), 63-73.
[http://dx.doi.org/10.1016/j.virusres.2007.02.015] [PMID:  17397958] 
[357] 
Xiong, B.; Gui, C-S.; Xu, X-Y.; Luo, C.; Chen, J.; Luo, H-B.; Chen, L-L.; Li, G-W.; Sun, T.; Yu, C-Y.; Yue, L-D.; Duan, W-H.; Shen, J-K.; Qin, L.; Shi, T-L.; Li, Y-X.; Chen, K-X.; Luo, X-M.; Shen, X.; Shen, J-H.; Jiang, H-L. A 3D model of SARS_CoV 3CL proteinase and its inhibitors design by virtual screening. Acta Pharmacol. Sin.,  2003, 24(6), 497-504.
[PMID:  12791174] 
[358] 
Clark, R.D.; Strizhev, A.; Leonard, J.M.; Blake, J.F.; Matthew, J.B. Consensus scoring for ligand/protein interactions. J. Mol. Graph. Model.,  2002, 20(4), 281-295.
[http://dx.doi.org/10.1016/S1093-3263(01)00125-5] [PMID:  11858637] 
[359] 
Lee, V.S.; Wittayanarakul, K.; Remsungnen, T.; Parasuk, V.; Sompornpisut, P.; Chantratita, W.; Sangma, C.; Vannarat, S.; Srichaikul, P.; Hannongbua, S. Structure and dynamics of sars coronavirus proteinase: the primary key to the designing and screening for anti-sars drugs. Sci. Asia,  2003, 29, 181-188.
[http://dx.doi.org/10.2306/scienceasia1513-1874.2003.29.181] 
[360] 
Nicklaus, M.C. National Cancer Institute.,  Available from:
                    https://cactus.nci.nih.gov/download/nci/
[361] 
Toney, J.H.; Navas-Martín, S.; Weiss, S.R.; Koeller, A. Sabadinine: a potential non-peptide anti-severe acute-respiratory-syndrome agent identified using structure-aided design. J. Med. Chem.,  2004, 47(5), 1079-1080.
[http://dx.doi.org/10.1021/jm034137m] [PMID:  14971887] 
[362] 
Sirois, S.; Wei, D-Q.; Du, Q.; Chou, K-C. Virtual screening for SARS-CoV protease based on KZ7088 pharmacophore points. J. Chem. Inf. Comput. Sci.,  2004, 44(3), 1111-1122.
[http://dx.doi.org/10.1021/ci034270n] [PMID:  15154780] 
[363] 
Chou, K.C.; Wei, D.Q.; Zhong, W.Z. Binding mechanism of coronavirus main proteinase with ligands and its implication to drug design against SARS. Biochem. Biophys. Res. Commun.,  2003, 308(1), 148-151.
[http://dx.doi.org/10.1016/S0006-291X(03)01342-1] [PMID:  12890493] 
[364] 
Pillaiyar, T.; Manickam, M.; Namasivayam, V.; Hayashi, Y.; Jung, S-H. An overview of severe acute respiratory syndrome-coronavirus (sars-cov) 3cl protease inhibitors: peptidomimetics and small molecule chemotherapy. J. Med. Chem.,  2016, 59(14), 6595-6628.
[http://dx.doi.org/10.1021/acs.jmedchem.5b01461] [PMID:  26878082] 
[365] 
Shao, Y-M.; Yang, W-B.; Peng, H-P.; Hsu, M-F.; Tsai, K-C.; Kuo, T-H.; Wang, A.H-J.; Liang, P-H.; Lin, C-H.; Yang, A-S.; Wong, C-H. Structure-based design and synthesis of highly potent SARS-CoV 3CL protease inhibitors. ChemBioChem,  2007, 8(14), 1654-1657.
[http://dx.doi.org/10.1002/cbic.200700254] [PMID:  17722121] 
[366] 
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] 
[367] 
ChemBridge Corporation.  Available from:
                    https://www.chembridge.com
[368] 
Nguyen, T.T.H.; Ryu, H-J.; Lee, S-H.; Hwang, S.; Breton, V.; Rhee, J.H.; Kim, D. Virtual screening identification of novel severe acute respiratory syndrome 3C-like protease inhibitors and in vitro confirmation. Bioorg. Med. Chem. Lett.,  2011, 21(10), 3088-3091.
[http://dx.doi.org/10.1016/j.bmcl.2011.03.034] [PMID:  21470860] 
[369] 
Jacq, N.; Salzemann, J.; Jacq, F.; Legré, Y.; Medernach, E.; Montagnat, J.; Maaß, A.; Reichstadt, M.; Schwichtenberg, H.; Sridhar, M.; Kasam, V.; Zimmermann, M.; Hofmann, M.; Breton, V. Grid-enabled virtual screening against malaria. J. Grid Comput.,  2008, 6, 29-43.
[http://dx.doi.org/10.1007/s10723-007-9085-5] 
[370] 
Lee, H.; Mittal, A.; Patel, K.; Gatuz, J.L.; Truong, L.; Torres, J.; Mulhearn, D.C.; Johnson, M.E. Identification of novel drug scaffolds for inhibition of SARS-CoV 3-Chymotrypsin-like protease using virtual and high-throughput screenings. Bioorg. Med. Chem.,  2014, 22(1), 167-177.
[http://dx.doi.org/10.1016/j.bmc.2013.11.041] [PMID:  24332657] 
[371] 
Graves, A.P.; Shivakumar, D.M.; Boyce, S.E.; Jacobson, M.P.; Case, D.A.; Shoichet, B.K. Rescoring docking hit lists for model cavity sites: predictions and experimental testing. J. Mol. Biol.,  2008, 377(3), 914-934.
[http://dx.doi.org/10.1016/j.jmb.2008.01.049] [PMID:  18280498] 
[372] 
Ton, A-T.; Gentile, F.; Hsing, M.; Ban, F.; Cherkasov, A. Rapid identification of potential inhibitors of sars-cov-2 main protease by deep docking of 1.3 billion compounds. Mol. Inform.,  2020, 39(8)e2000028
[http://dx.doi.org/10.1002/minf.202000028] [PMID:  32162456] 
[373] 
Gentile, F.; Agrawal, V.; Hsing, M.; Ban, F.; Norinder, U.; Gleave, M.E.; Cherkasov, A. Deep docking - a deep learning approach for virtual screening of big chemical datasets. bioRxiv, 2019.
[374] 
Kandeel, M.; Al-Nazawi, M. Virtual screening and repurposing of FDA approved drugs against COVID-19 main protease. Life Sci.,  2020, 251117627
[http://dx.doi.org/10.1016/j.lfs.2020.117627] [PMID:  32251634] 
[375] 
Khan, S.A.; Zia, K.; Ashraf, S.; Uddin, R.; Ul-Haq, Z. Identification of chymotrypsin-like protease inhibitors of SARS-CoV-2 via integrated computational approach. J. Biomol. Struct. Dyn.,  2020, 1-10. [Online ahead of Print
[PMID:  32238094] 
[376] 
Wu, C.; Liu, Y.; Yang, Y.; Zhang, P.; Zhong, W.; Wang, Y.; Wang, Q.; Xu, Y.; Li, M.; Li, X.; Zheng, M.; Chen, L.; Li, H. Analysis of therapeutic targets for SARS-CoV-2 and discovery of potential drugs by computational methods. Acta Pharm. Sin. B,  2020, 10(5), 766-788.
[http://dx.doi.org/10.1016/j.apsb.2020.02.008] [PMID:  32292689] 
[377] 
Khan, R.J.; Jha, R.K.; Amera, G.M.; Jain, M.; Singh, E.; Pathak, A.; Singh, R.P.; Muthukumaran, J.; Singh, A.K. Targeting SARS-CoV-2: a systematic drug repurposing approach to identify promising inhibitors against 3C-like proteinase and 2′-O-ribose methyltransferase. J. Biomol. Struct. Dyn.,  2020, 1-14. [Online ahead of Print
[PMID:  32266873] 
[378] 
Elfiky, A.A. SARS-CoV-2 RNA dependent RNA polymerase (RdRp) targeting: an in silico perspective. J. Biomol. Struct. Dyn.,  2020, 1-9. [Online ahead of Print
[http://dx.doi.org/10.1080/07391102.2020.1761882 ] [PMID:  32338164] 
[379] 
Gimeno, A.; Mestres-Truyol, J.; Ojeda-Montes, M.J.; Macip, G.; Saldivar-Espinoza, B.; Cereto-Massagué, A.; Pujadas, G.; Garcia-Vallvé, S. Prediction of novel inhibitors of the main protease (m-pro) of sars-cov-2 through consensus docking and drug reposition. Int. J. Mol. Sci.,  2020, 21(11), 3793-3821.
[http://dx.doi.org/10.3390/ijms21113793] [PMID:  32471205] 
[380] 
Liu, X.; Wang, X-J. Potential inhibitors against 2019-nCoV coronavirus M protease from clinically approved medicines. J. Genet. Genomics,  2020, 47(2), 119-121.
[http://dx.doi.org/10.1016/j.jgg.2020.02.001] [PMID:  32173287] 
[381] 
Calligari, P.; Bobone, S.; Ricci, G.; Bocedi, A. Molecular investigation of sars-cov-2 proteins and their interactions with antiviral drugs. Viruses,  2020, 12(4), 445.
[http://dx.doi.org/10.3390/v12040445] [PMID:  32295237] 
[382] 
Joshi, T.; Joshi, T.; Sharma, P.; Mathpal, S.; Pundir, H.; Bhatt, V.; Chandra, S. In silico screening of natural compounds against COVID-19 by targeting Mpro and ACE2 using molecular docking. Eur. Rev. Med. Pharmacol. Sci.,  2020, 24(8), 4529-4536.
[PMID:  32373991] 
[383] 
Tsuji, M. Potential anti-SARS-CoV-2 drug candidates identified through virtual screening of the ChEMBL database for compounds that target the main coronavirus protease. FEBS Open Bio,  2020, 10(6), 995-1004.
[http://dx.doi.org/10.1002/2211-5463.12875] [PMID:  32374074] 
[384] 
Gyebi, G.A.; Ogunro, O.B.; Adegunloye, A.P.; Ogunyemi, O.M.; Afolabi, S.O. Potential inhibitors of coronavirus 3-chymotrypsin-like protease (3CLpro): an in silico screening of alkaloids and terpenoids from African medicinal plants. J. Biomol. Struct. Dyn.,  2020, 1-13. [Online ahead of Print
[PMID: 32367767] 
[385] 
Gentile, D.; Patamia, V.; Scala, A.; Sciortino, M.T.; Piperno, A.; Rescifina, A. Putative inhibitors of sars-cov-2 main protease from a library of marine natural products: a virtual screening and molecular modeling study. Mar. Drugs,  2020, 18(4), 18.
[http://dx.doi.org/10.3390/md18040225] [PMID:  32340389] 
[386] 
Eleftheriou, P.; Amanatidou, D.; Petrou, A.; Geronikaki, A. In silico evaluation of the effectivity of approved protease inhibitors against the main protease of the novel sars-cov-2 virus. Molecules,  2020, 25(11), 25.
[http://dx.doi.org/10.3390/molecules25112529] [PMID:  32485894] 
[387] 
ZINC catalog: DrugBank FDA only..  Available from:
                    https://zinc.docking.org/catalogs/dbfda/
[388] 
Law, V.; Knox, C.; Djoumbou, Y.; Jewison, T.; Guo, A.C.; Liu, Y.; Maciejewski, A.; Arndt, D.; Wilson, M.; Neveu, V.; Tang, A.; Gabriel, G.; Ly, C.; Adamjee, S.; Dame, Z.T.; Han, B.; Zhou, Y.; Wishart, D.S. DrugBank 4.0: shedding new light on drug metabolism. Nucleic Acids Res.,  2014, 42(Database issue), D1091-D1097.
[http://dx.doi.org/10.1093/nar/gkt1068] [PMID:  24203711] 
[389] 
ZINC catalog: DrugBank-approved..  Available from:
                    https://zinc.docking.org/catalogs/dbap/
[390] 
Voevodin, V.V.; Antonov, A.S.; Nikitenko, D.A.; Shvets, P.A.; Sobolev, S.I.; Sidorov, I.Y.; Stefanov, K.S.; Voevodin, V.V.; Zhumatiy, S.A. Supercomputer lomonosov-2: large scale, deep monitoring and fine analytics for the user community. Supercomput. Front. Innov.,  2019, 6, 4-11.
[391] 
O’Boyle, N.M.; Banck, M.; James, C.A.; Morley, C.; Vandermeersch, T.; Hutchison, G.R. Open babel: An open chemical toolbox. J. Cheminform.,  2011, 3, 33.
[http://dx.doi.org/10.1186/1758-2946-3-33] [PMID:  21982300] 
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DOI https://dx.doi.org/10.2174/1568026620666201207095626	Print ISSN 1568-0266
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