Molecular Modeling Techniques Applied to the Design of Multitarget
Drugs: Methods and Applications

Larissa    Henriques Evangelista    Castro; Carlos    Mauricio R.    Sant'Anna
Abstract

Multifactorial diseases, such as cancer and diabetes present a challenge for the traditional “one-target, one disease” paradigm due to their complex pathogenic mechanisms. Although a combination of drugs can be used, a multitarget drug may be a better choice due to its efficacy, lower adverse effects and lower chance of resistance development. The computer-based design of these multitarget drugs can explore the same techniques used for single-target drug design, but the difficulties associated with the obtention of drugs that are capable of modulating two or more targets with similar efficacy impose new challenges, whose solutions involve the adaptation of known techniques and also to the development of new ones, including machine-learning approaches. In this review, some SBDD and LBDD techniques for the multitarget drug design are discussed, together with some cases where the application of such techniques led to effective multitarget ligands.
Keywords: Multitarget Drug design, SDBB, LBDD, Molecular hybridization, Artificial intelligence, Machine learning.
Graphical Abstract

[1] 
Tamimi, N.A.M.; Ellis, P. Drug development: from concept to marketing! Nephron Clin. Pract.,  2009, 113(3), c125-c131.
[http://dx.doi.org/10.1159/000232592] [PMID: 19729922] 
[2] 
Keseru, G.M.; Makara, G.M. Hit discovery and hit-to-lead approaches. Drug Discov. Today,  2006, 11(15-16), 741-748.
[http://dx.doi.org/10.1016/j.drudis.2006.06.016] [PMID: 16846802] 
[3] 
Payne, R.A. The epidemiology of polypharmacy. Clin. Med. (Lond.),  2016, 16(5), 465-469.
[http://dx.doi.org/10.7861/clinmedicine.16-5-465] [PMID: 27697812] 
[4] 
Anighoro, A.; Bajorath, J.; Rastelli, G. Polypharmacology: challenges and opportunities in drug discovery. J. Med. Chem.,  2014, 57(19), 7874-7887.
[http://dx.doi.org/10.1021/jm5006463] [PMID: 24946140] 
[5] 
Peters, J.U. Polypharmacology - foe or friend? J. Med. Chem.,  2013, 56(22), 8955-8971.
[http://dx.doi.org/10.1021/jm400856t] [PMID: 23919353] 
[6] 
Cavalli, A.; Bolognesi, M.L.; Mìnarini, A.; Rosini, M.; Tumiatti, V.; Recanatini, M.; Melchiorre, C. Multi-target-directed ligands to combat neurodegenerative diseases. J. Med. Chem.,  2008, 51(3), 347-372.
[http://dx.doi.org/10.1021/jm7009364] [PMID: 18181565] 
[7] 
Morphy, R.; Rankovic, Z. Designed multiple ligands. An emerging drug discovery paradigm. J. Med. Chem.,  2005, 48(21), 6523-6543.
[http://dx.doi.org/10.1021/jm058225d] [PMID: 16220969] 
[8] 
Morphy, R.; Kay, C.; Rankovic, Z. From magic bullets to designed multiple ligands. Drug Discov. Today,  2004, 9(15), 641-651.
[http://dx.doi.org/10.1016/S1359-6446(04)03163-0] [PMID: 15279847] 
[9] 
Van Drie, J.H. Computer-aided drug design: the next 20 years. J. Comput. Aided Mol. Des.,  2007, 21(10-11), 591-601.
[http://dx.doi.org/10.1007/s10822-007-9142-y] [PMID: 17989929] 
[10] 
Jorgensen, W.L. The many roles of computation in drug discovery. Science,  2004, 303(5665), 1813-1818.
[http://dx.doi.org/10.1126/science.1096361] [PMID: 15031495] 
[11] 
Talele, T.T.; Khedkar, S.A.; Rigby, A.C. Successful applications of computer aided drug discovery: moving drugs from concept to the clinic. Curr. Top. Med. Chem.,  2010, 10(1), 127-141.
[http://dx.doi.org/10.2174/156802610790232251] [PMID: 19929824] 
[12] 
Muegge, I.; Bergner, A.; Kriegl, J.M. Computer-aided drug design at Boehringer Ingelheim. J. Comput. Aided Mol. Des.,  2017, 31(3), 275-285.
[http://dx.doi.org/10.1007/s10822-016-9975-3] [PMID: 27650777] 
[13] 
Brown, F.K.; Sherer, E.C.; Johnson, S.A.; Holloway, M.K.; Sherborne, B.S. The evolution of drug design at Merck Research Laboratories. J. Comput. Aided Mol. Des.,  2017, 31(3), 255-266.
[http://dx.doi.org/10.1007/s10822-016-9993-1] [PMID: 27878643] 
[14] 
Müller, K. Three decades of structure- and property-based molecular design. Chimia (Aarau),  2014, 68(7-8), 472-482.
[http://dx.doi.org/10.2533/chimia.2014.472] [PMID: 25437386] 
[15] 
Merz, K.M.; Ringe, D.; Reynolds, C.H. Drug design: Structure- and ligand-based approaches; Cambridge University Press, 2010. 
[http://dx.doi.org/10.1017/CBO9780511730412] 
[16] 
Verma, S.; Prabhakar, Y.S. Target based drug design - a reality in virtual sphere. Curr. Med. Chem.,  2015, 22(13), 1603-1630.
[http://dx.doi.org/10.2174/0929867322666150209151209] [PMID: 25666805] 
[17] 
Danishuddin, ; Khan, A.U. Descriptors and their selection methods in QSAR analysis: paradigm for drug design. Drug Discov. Today,  2016, 21(8), 1291-1302.
[http://dx.doi.org/10.1016/j.drudis.2016.06.013] [PMID: 27326911] 
[18] 
Cherkasov, A.; Muratov, E.N.; Fourches, D.; Varnek, A.; Baskin, I.I.; Cronin, M.; Dearden, J.; Gramatica, P.; Martin, Y.C.; Todeschini, R.; Consonni, V.; Kuz’min, V.E.; Cramer, R.; Benigni, R.; Yang, C.; Rathman, J.; Terfloth, L.; Gasteiger, J.; Richard, A.; Tropsha, A. QSAR modeling: where have you been? Where are you going to? J. Med. Chem.,  2014, 57(12), 4977-5010.
[http://dx.doi.org/10.1021/jm4004285] [PMID: 24351051] 
[19] 
Batool, M.; Ahmad, B.; Choi, S. A Structure-based drug discovery paradigm. Int. J. Mol. Sci.,  2019, 20(11), 20.
[http://dx.doi.org/10.3390/ijms20112783] [PMID: 31174387] 
[20] 
Anderson, A.C. The process of structure-based drug design. Chem. Biol.,  2003, 10(9), 787-797.
[http://dx.doi.org/10.1016/j.chembiol.2003.09.002] [PMID: 14522049] 
[21] 
da Silva Rocha, S.F.L.; Olanda, C.G.; Fokoue, H.H.; Sant’Anna, C.M.R. Virtual screening techniques in drug discovery: review and recent applications. Curr. Top. Med. Chem.,  2019, 19(19), 1751-1767.
[http://dx.doi.org/10.2174/1568026619666190816101948] [PMID: 31418662] 
[22] 
Schneider, G. Virtual screening: an endless staircase? Nat. Rev. Drug Discov.,  2010, 9(4), 273-276.
[http://dx.doi.org/10.1038/nrd3139] [PMID: 20357802] 
[23] 
Lavecchia, A.; Di Giovanni, C. Virtual screening strategies in drug discovery: a critical review. Curr. Med. Chem.,  2013, 20(23), 2839-2860.
[http://dx.doi.org/10.2174/09298673113209990001] [PMID: 23651302] 
[24] 
Berendsen, H.J.C. Simulating the physical world: hierachical modeling from quantum mechanics to fluid dynamics; Cambridge University Press: Cambridge, 2007. 
[http://dx.doi.org/10.1017/CBO9780511815348] 
[25] 
van Gunsteren, W.F.; Daura, X.; Hansen, N.; Mark, A.E.; Oostenbrink, C.; Riniker, S.; Smith, L.J. Validation of molecular simulation: an overview of issues. Angew. Chem. Int. Ed. Engl.,  2018, 57(4), 884-902.
[http://dx.doi.org/10.1002/anie.201702945] [PMID: 28682472] 
[26] 
Kamb, A.; Wee, S.; Lengauer, C. Why is cancer drug discovery so difficult? Nat. Rev. Drug Discov.,  2007, 6(2), 115-120.
[http://dx.doi.org/10.1038/nrd2155] [PMID: 17159925] 
[27] 
Lu, J.J.; Pan, W.; Hu, Y.J.; Wang, Y.T. Multitarget drugs: the trend of drug research and development. PLoS One,  2012, 7, 40262.
[http://dx.doi.org/10.1371/journal.pone.0040262] 
[28] 
Csermely, P.; Agoston, V.; Pongor, S. The efficiency of multi-target drugs: the network approach might help drug design. Trends Pharmacol. Sci.,  2005, 26(4), 178-182.
[http://dx.doi.org/10.1016/j.tips.2005.02.007] [PMID: 15808341] 
[29] 
Kakarala, K.K.; Jamil, K. Identification of novel allosteric binding sites and multi-targeted allosteric inhibitors of receptor and non-receptor tyrosine kinases using a computational approach. J. Biomol. Struct. Dyn.,  2021, 1-22.
[http://dx.doi.org/10.1080/07391102.2021.1891140] [PMID: 33682622] 
[30] 
Zhang, M.; Quan, H.; Fu, L.; Li, Y.; Fu, H.; Lou, L. Third-generation EGFR inhibitor HS-10296 in combination with famitinib, a multi-targeted tyrosine kinase inhibitor, exerts synergistic antitumor effects through enhanced inhibition of downstream signaling in EGFR-mutant non-small cell lung cancer cells. Thorac. Cancer,  2021, 12(8), 1210-1218.
[http://dx.doi.org/10.1111/1759-7714.13902] [PMID: 33656275] 
[31] 
Grover, M.; Behl, T.; Sachdeva, M.; Bungao, S.; Aleya, L.; Setia, D. Focus on multi-targeted role of curcumin: a boon in therapeutic paradigm. Environ. Sci. Pollut. Res. Int.,  2021, 28(15), 18893-18907.
[http://dx.doi.org/10.1007/s11356-021-12809-w] [PMID: 33595796] 
[32] 
Petrelli, A.; Giordano, S. From single- to multi-target drugs in cancer therapy: when aspecificity becomes an advantage. Curr. Med. Chem.,  2008, 15(5), 422-432.
[http://dx.doi.org/10.2174/092986708783503212] [PMID: 18288997] 
[33] 
Derosa, G.; Cicero, A.F.G.; D’Angelo, A.; Gaddi, A.; Ciccarelli, L.; Piccinni, M.N.; Salvadeo, S.A.T.; Pricolo, F.; Ferrari, I.; Gravina, A.; Ragonesi, P.D. Effects of 1 year of treatment with pioglitazone or rosiglitazone added to glimepiride on lipoprotein (a) and homocysteine concentrations in patients with type 2 diabetes mellitus and metabolic syndrome: a multicenter, randomized, double-blind, controlled clinical trial. Clin. Ther.,  2006, 28(5), 679-688.
[http://dx.doi.org/10.1016/j.clinthera.2006.05.012] [PMID: 16861090] 
[34] 
Vuylsteke, V.; Chastain, L.M.; Maggu, G.A.; Brown, C. Imeglimin: a potential new Multi-target drug for type 2 diabetes. Drugs R D.,  2015, 15(3), 227-232.
[http://dx.doi.org/10.1007/s40268-015-0099-3] [PMID: 26254210] 
[35] 
Makhoba, X.H.; Viegas, C., Jr; Mosa, R.A.; Viegas, F.P.D.; Pooe, O.J. Potential impact of the multi-target drug approach in the treatment of some complex diseases. Drug Des. Devel. Ther.,  2020, 14, 3235-3249.
[http://dx.doi.org/10.2147/DDDT.S257494] [PMID: 32884235] 
[36] 
Amani, A.; Alizadeh, M.R.; Yaghoubi, H.; Nohtani, M. Novel multi-targeted nanoparticles for targeted co-delivery of nucleic acid and chemotherapeutic agents to breast cancer tissues. Mater. Sci. Eng. C,  2021, 118, 111494.
[http://dx.doi.org/10.1016/j.msec.2020.111494] [PMID: 33255061] 
[37] 
Mishra, S.; Rajput, M.S.; Rathore, D.; Dahima, R. Ligand and structure-based computational designing of multitarget molecules directing FFAR-1, FFAR-4 and ppar-g as modulators of insulin receptor activity. J. Biomol. Struct. Dyn., 2021. [Online ahead of print]
[38] 
Julius, A.; Hopper, W. A non-invasive, multi-target approach to treat diabetic retinopathy. Biomed. Pharmacother.,  2019, 109, 708-715.
[http://dx.doi.org/10.1016/j.biopha.2018.10.185] [PMID: 30551523] 
[39] 
Ramsay, R.R.; Popovic-Nikolic, M.R.; Nikolic, K.; Uliassi, E.; Bolognesi, M.L. A perspective on multi-target drug discovery and design for complex diseases. Clin. Transl. Med.,  2018, 7(1), 3-3.
[http://dx.doi.org/10.1186/s40169-017-0181-2] [PMID: 29340951] 
[40] 
Ali, H.S.; Chakravorty, A.; Kalayan, J.; de Visser, S.P.; Henchman, R.H. Energy-entropy method using multiscale cell correlation to calculate binding free energies in the SAMPL8 host-guest challenge. J. Comput. Aided Mol. Des.,  2021, 35(8), 911-921.
[http://dx.doi.org/10.1007/s10822-021-00406-5] [PMID: 34264476] 
[41] 
Mobley, D.L.; Gilson, M.K. Predicting binding free energies: frontiers and benchmarks. Annu. Rev. Biophys.,  2017, 46, 531-558.
[http://dx.doi.org/10.1146/annurev-biophys-070816-033654] [PMID: 28399632] 
[42] 
Viegas-Junior, C.; Danuello, A.; da Silva Bolzani, V.; Barreiro, E.J.; Fraga, C.A.M. Molecular hybridization: a useful tool in the design of new drug prototypes. Curr. Med. Chem.,  2007, 14(17), 1829-1852.
[http://dx.doi.org/10.2174/092986707781058805] [PMID: 17627520] 
[43] 
Ivasiv, V.; Albertini, C.; Gonçalves, A.E.; Rossi, M.; Bolognesi, M.L. Molecular hybridization as a tool for designing multitarget drug candidates for complex diseases. Curr. Top. Med. Chem.,  2019, 19(19), 1694-1711.
[http://dx.doi.org/10.2174/1568026619666190619115735] [PMID: 31237210] 
[44] 
Zhou, J.; Jiang, X.; He, S.; Jiang, H.; Feng, F.; Liu, W.; Qu, W.; Sun, H. Rational design of multitarget-directed ligands: strategies and emerging paradigms. J. Med. Chem.,  2019, 62(20), 8881-8914.
[http://dx.doi.org/10.1021/acs.jmedchem.9b00017] [PMID: 31082225] 
[45] 
Kuduk, S.D.; Zheng, F.F.; Sepp-Lorenzino, L.; Rosen, N.; Danishefsky, S.J. Synthesis and evaluation of geldanamycin-estradiol hybrids. Bioorg. Med. Chem. Lett.,  1999, 9(9), 1233-1238.
[http://dx.doi.org/10.1016/S0960-894X(99)00185-7] [PMID: 10340605] 
[46] 
Sterling, J.; Herzig, Y.; Goren, T.; Finkelstein, N.; Lerner, D.; Goldenberg, W.; Miskolczi, I.; Molnar, S.; Rantal, F.; Tamas, T.; Toth, G.; Zagyva, A.; Zekany, A.; Finberg, J.; Lavian, G.; Gross, A.; Friedman, R.; Razin, M.; Huang, W.; Krais, B.; Chorev, M.; Youdim, M.B.; Weinstock, M. Novel dual inhibitors of AChE and MAO derived from hydroxy aminoindan and phenethylamine as potential treatment for Alzheimer’s disease. J. Med. Chem.,  2002, 45(24), 5260-5279.
[http://dx.doi.org/10.1021/jm020120c] [PMID: 12431053] 
[47] 
Pourabdi, L.; Khoobi, M.; Nadri, H.; Moradi, A.; Moghadam, F.H.; Emami, S.; Mojtahedi, M.M.; Haririan, I.; Forootanfar, H.; Ameri, A.; Foroumadi, A.; Shafiee, A. Synthesis and structure-activity relationship study of tacrine-based pyrano[2,3-c]pyrazoles targeting AChE/BuChE and 15-LOX. Eur. J. Med. Chem.,  2016, 123, 298-308.
[http://dx.doi.org/10.1016/j.ejmech.2016.07.043] [PMID: 27484515] 
[48] 
Lazar, C.; Kluczyk, A.; Kiyota, T.; Konishi, Y. Drug evolution concept in drug design: 1. Hybridization method. J. Med. Chem.,  2004, 47(27), 6973-6982.
[http://dx.doi.org/10.1021/jm049637+] [PMID: 15615546] 
[49] 
de Oliveira Pedrosa, M.; Duarte da Cruz, R.M.; de Oliveira Viana, J.; de Moura, R.O.; Ishiki, H.M.; Barbosa Filho, J.M.; Diniz, M.F.; Scotti, M.T.; Scotti, L.; Bezerra Mendonca, F.J. Hybrid compounds as direct multitarget ligands: a review. Curr. Top. Med. Chem.,  2017, 17(9), 1044-1079.
[http://dx.doi.org/10.2174/1568026616666160927160620] [PMID: 27697048] 
[50] 
Schmid, A.; Blank, L.M. Systems biology: hypothesis-driven omics integration. Nat. Chem. Biol.,  2010, 6(7), 485-487.
[http://dx.doi.org/10.1038/nchembio.398] [PMID: 20559314] 
[51] 
Joyce, A.R.; Palsson, B.Ø. The model organism as a system: integrating ‘omics’ data sets. Nat. Rev. Mol. Cell Biol.,  2006, 7(3), 198-210.
[http://dx.doi.org/10.1038/nrm1857] [PMID: 16496022] 
[52] 
Zhao, S.; Iyengar, R. Systems pharmacology: network analysis to identify multiscale mechanisms of drug action. Annu. Rev. Pharmacol. Toxicol.,  2012, 52, 505-521.
[http://dx.doi.org/10.1146/annurev-pharmtox-010611-134520] [PMID: 22235860] 
[53] 
Bantscheff, M.; Drewes, G. Chemoproteomic approaches to drug target identification and drug profiling. Bioorg. Med. Chem.,  2012, 20(6), 1973-1978.
[http://dx.doi.org/10.1016/j.bmc.2011.11.003] [PMID: 22130419] 
[54] 
Hu, Y.; Zhao, T.; Zhang, N.; Zhang, Y.; Cheng, L. A review of recent advances and research on drug target identification methods. Curr. Drug Metab.,  2019, 20(3), 209-216.
[http://dx.doi.org/10.2174/1389200219666180925091851] [PMID: 30251599] 
[55] 
Katsila, T.; Spyroulias, G.A.; Patrinos, G.P.; Matsoukas, M.T. Computational approaches in target identification and drug discovery. Comput. Struct. Biotechnol. J.,  2016, 14, 177-184.
[http://dx.doi.org/10.1016/j.csbj.2016.04.004] [PMID: 27293534] 
[56] 
Rognan, D. Structure-based approaches to target fishing and ligand profiling. Mol. Inform.,  2010, 29(3), 176-187.
[http://dx.doi.org/10.1002/minf.200900081] [PMID: 27462761] 
[57] 
Li, H.; Yap, C.W.; Ung, C.Y.; Xue, Y.; Li, Z.R.; Han, L.Y.; Lin, H.H.; Chen, Y.Z. Machine learning approaches for predicting compounds that interact with therapeutic and ADMET related proteins. J. Pharm. Sci.,  2007, 96(11), 2838-2860.
[http://dx.doi.org/10.1002/jps.20985] [PMID: 17786989] 
[58] 
Nidhi, ; Glick, M.; Davies, J.W.; Jenkins, J.L. Prediction of biological targets for compounds using multiple-category Bayesian models trained on chemogenomics databases. J. Chem. Inf. Model.,  2006, 46(3), 1124-1133.
[http://dx.doi.org/10.1021/ci060003g] [PMID: 16711732] 
[59] 
Li, H.; Gao, Z.; Kang, L.; Zhang, H.; Yang, K.; Yu, K.; Luo, X.; Zhu, W.; Chen, K.; Shen, J.; Wang, X.; Jiang, H. TarFisDock: a web server for identifying drug targets with docking approach. Nucleic Acids Res.,  2006, 34(Web Server issue), W219-W224.
[http://dx.doi.org/10.1093/nar/gkl114] [PMID: 16844997] 
[60] 
Cheng, T.; Li, Q.; Wang, Y.; Bryant, S.H. Identifying compound-target associations by combining bioactivity profile similarity search and public databases mining. J. Chem. Inf. Model.,  2011, 51(9), 2440-2448.
[http://dx.doi.org/10.1021/ci200192v] [PMID: 21834535] 
[61] 
Cao, R.; Wang, Y. Predicting molecular targets for small-molecule drugs with a ligand-based interaction fingerprint approach. ChemMedChem,  2016, 11(12), 1352-1361.
[http://dx.doi.org/10.1002/cmdc.201500228] [PMID: 26222196] 
[62] 
Khan, F.I.; Wei, D.Q.; Gu, K.R.; Hassan, M.I.; Tabrez, S. Current updates on computer aided protein modeling and designing. Int. J. Biol. Macromol.,  2016, 85, 48-62.
[http://dx.doi.org/10.1016/j.ijbiomac.2015.12.072] [PMID: 26730484] 
[63] 
Takeda-Shitaka, M.; Takaya, D.; Chiba, C.; Tanaka, H.; Umeyama, H. Protein structure prediction in structure based drug design. Curr. Med. Chem.,  2004, 11(5), 551-558.
[http://dx.doi.org/10.2174/0929867043455837] [PMID: 15032603] 
[64] 
Wang, T.; Qiao, Y.; Ding, W.; Mao, W.; Zhou, Y.; Gong, H. Improved fragment sampling for ab initio protein structure prediction using deep neural networks. Nat. Mach. Intell.,  2019, 1, 347-355.
[http://dx.doi.org/10.1038/s42256-019-0075-7] 
[65] 
Berman, H.; Henrick, K.; Nakamura, H. Announcing the worldwide protein data bank. Nat. Struct. Biol.,  2003, 10(12), 980.
[http://dx.doi.org/10.1038/nsb1203-980] [PMID: 14634627] 
[66] 
PDB consortium. Protein data bank: the single global archive for 3D macromolecular structure data. Nucleic Acids Res.,  2019, 47(D1), D520-D528.
[http://dx.doi.org/10.1093/nar/gky949] [PMID: 30357364] 
[67] 
Goodford, P.J. A computational procedure for determining energetically favorable binding sites on biologically important macromolecules. J. Med. Chem.,  1985, 28(7), 849-857.
[http://dx.doi.org/10.1021/jm00145a002] [PMID: 3892003] 
[68] 
Laurie, A.T.R.; Jackson, R.M. Q-SiteFinder: an energy-based method for the prediction of protein-ligand binding sites. Bioinformatics,  2005, 21(9), 1908-1916.
[http://dx.doi.org/10.1093/bioinformatics/bti315] [PMID: 15701681] 
[69] 
Ngan, C.H.; Bohnuud, T.; Mottarella, S.E.; Beglov, D.; Villar, E.A.; Hall, D.R.; Kozakov, D.; Vajda, S. FTMAP: extended protein mapping with user-selected probe molecules. Nucleic Acids Res.,  2012, 40(Web Server issue), W271-W275.
[http://dx.doi.org/10.1093/nar/gks441] [PMID: 22589414] 
[70] 
Huang, B.; Schroeder, M. LIGSITEcsc: predicting ligand binding sites using the connolly surface and degree of conservation. BMC Struct. Biol.,  2006, 6, 19.
[http://dx.doi.org/10.1186/1472-6807-6-19] [PMID: 16995956] 
[71] 
Simões, T.; Lopes, D.; Dias, S.; Fernandes, F.; Pereira, J.; Jorge, J.; Bajaj, C.; Gomes, A. Geometric detection algorithms for cavities on protein surfaces in molecular graphics: a survey. Comput. Graph. Forum,  2017, 36(8), 643-683.
[http://dx.doi.org/10.1111/cgf.13158] [PMID: 29520122] 
[72] 
Macari, G.; Toti, D.; Polticelli, F. Computational methods and tools for binding site recognition between proteins and small molecules: from classical geometrical approaches to modern machine learning strategies. J. Comput. Aided Mol. Des.,  2019, 33(10), 887-903.
[http://dx.doi.org/10.1007/s10822-019-00235-7] [PMID: 31628659] 
[73] 
Marchand, J.R.; Pirard, B.; Ertl, P.; Sirockin, F. CAVIAR: a method for automatic cavity detection, description and decomposition into subcavities. J. Comput. Aided Mol. Des.,  2021, 35(6), 737-750.
[http://dx.doi.org/10.1007/s10822-021-00390-w] [PMID: 34050420] 
[74] 
Chaudhary, K.K.; Mishra, N. A review on molecular docking: novel tool for drug discovery design. JSM Chem.,  2016, 4, 1029.
[75] 
Fradera, X.; Babaoglu, K. Overview of methods and strategies for conducting virtual small molecule screening. Curr. Protoc. Chem. Biol.,  2017, 9(3), 196-212.
[http://dx.doi.org/10.1002/cpch.27] [PMID: 28910858] 
[76] 
Casbarra, L.; Procacci, P. Binding free energy predictions in host-guest systems using Autodock4. A retrospective analysis on SAMPL6, SAMPL7 and SAMPL8 challenges. J. Comput. Aided Mol. Des.,  2021, 35(6), 721-729.
[http://dx.doi.org/10.1007/s10822-021-00388-4] [PMID: 34027592] 
[77] 
Salmaso, V.; Moro, S. Bridging molecular docking to molecular dynamics in exploring ligand-protein recognition process: an overview. Front. Pharmacol.,  2018, 9, 923.
[http://dx.doi.org/10.3389/fphar.2018.00923] [PMID: 30186166] 
[78] 
Hetényi, C.; van der Spoel, D. Blind docking of drug-sized compounds to proteins with up to a thousand residues. FEBS Lett.,  2006, 580(5), 1447-1450.
[http://dx.doi.org/10.1016/j.febslet.2006.01.074] [PMID: 16460734] 
[79] 
Hetényi, C.; van der Spoel, D. Efficient docking of peptides to proteins without prior knowledge of the binding site. Protein Sci.,  2002, 11(7), 1729-1737.
[http://dx.doi.org/10.1110/ps.0202302] [PMID: 12070326] 
[80] 
Morris, G.M.; Goodsell, D.S.; Huey, R.; Olson, A.J. Distributed automated docking of flexible ligands to proteins: parallel applications of AutoDock 2.4. J. Comput. Aided Mol. Des.,  1996, 10(4), 293-304.
[http://dx.doi.org/10.1007/BF00124499] [PMID: 8877701] 
[81] 
Ma, X.H.; Shi, Z.; Tan, C.; Jiang, Y.; Go, M.L.; Low, B.C.; Chen, Y.Z. In-silico approaches to multi-target drug discovery: computer aided multi-target drug design, multi-target virtual screening. Pharm. Res.,  2010, 27(5), 739-749.
[http://dx.doi.org/10.1007/s11095-010-0065-2] [PMID: 20221898] 
[82] 
Elisée, E.; Gapsys, V.; Mele, N.; Chaput, L.; Selwa, E.; de Groot, B.L.; Iorga, B.I. Performance evaluation of molecular docking and free energy calculations protocols using the D3R grand challenge 4 dataset. J. Comput. Aided Mol. Des.,  2019, 33(12), 1031-1043.
[http://dx.doi.org/10.1007/s10822-019-00232-w] [PMID: 31677003] 
[83] 
Selwa, E.; Martiny, V.Y.; Iorga, B.I. Molecular docking performance evaluated on the D3R Grand Challenge 2015 drug-like ligand datasets. J. Comput. Aided Mol. Des.,  2016, 30(9), 829-839.
[http://dx.doi.org/10.1007/s10822-016-9983-3] [PMID: 27699554] 
[84] 
Elokely, K.M.; Doerksen, R.J. Docking challenge: protein sampling and molecular docking performance. J. Chem. Inf. Model.,  2013, 53(8), 1934-1945.
[http://dx.doi.org/10.1021/ci400040d] [PMID: 23530568] 
[85] 
Lapillo, M.; Tuccinardi, T.; Martinelli, A.; Macchia, M.; Giordano, A.; Poli, G. Extensive reliability evaluation of docking-based target-fishing strategies. Int. J. Mol. Sci.,  2019, 20(5), 20.
[http://dx.doi.org/10.3390/ijms20051023] [PMID: 30818741] 
[86] 
Luo, Q.; Zhao, L.; Hu, J.; Jin, H.; Liu, Z.; Zhang, L. The scoring bias in reverse docking and the score normalization strategy to improve success rate of target fishing. PLoS One,  2017, 12(2), e0171433.
[http://dx.doi.org/10.1371/journal.pone.0171433] [PMID: 28196116] 
[87] 
Wójcikowski, M.; Ballester, P.J.; Siedlecki, P. Performance of machine-learning scoring functions in structure-based virtual screening. Sci. Rep.,  2017, 7, 46710.
[http://dx.doi.org/10.1038/srep46710] [PMID: 28440302] 
[88] 
Yasuo, N.; Sekijima, M. Improved method of structure-based virtual screening via interaction-energy-based learning J. Chem. Inf. Model.,  2019, 59(3), 1050-1061.
[http://dx.doi.org/10.1021/acs.jcim.8b00673] [PMID: 30808172] 
[89] 
Lee, A.; Kim, D. CRDS: consensus reverse docking system for target fishing. Bioinformatics,  2020, 36(3), 959-960.
[PMID: 31432077] 
[90] 
Lee, M.; Kim, D. Large-scale reverse docking profiles and their applications. BMC Bioinformatics,  2012, 13(Suppl. 17), S6.
[http://dx.doi.org/10.1186/1471-2105-13-S17-S6] [PMID: 23282219] 
[91] 
Sanders, M.P.A.; McGuire, R.; Roumen, L.; de Esch, I.J.P.; de Vlieg, J.; Klomp, J.P.G.; de Graaf, C. From the protein’s perspective: the benefits and challenges of protein Structure-based pharmacophore modeling. MedChemComm,  2012, 3, 28-38.
[http://dx.doi.org/10.1039/C1MD00210D] 
[92] 
Sanders, M.P.A.; Verhoeven, S.; de Graaf, C.; Roumen, L.; Vroling, B.; Nabuurs, S.B.; de Vlieg, J.; Klomp, J.P.G. Snooker: a structure-based pharmacophore generation tool applied to class A GPCRs. J. Chem. Inf. Model.,  2011, 51(9), 2277-2292.
[http://dx.doi.org/10.1021/ci200088d] [PMID: 21866955] 
[93] 
Ghanakota, P.; Carlson, H.A. Driving structure-based drug discovery through cosolvent molecular dynamics. J. Med. Chem.,  2016, 59(23), 10383-10399.
[http://dx.doi.org/10.1021/acs.jmedchem.6b00399] [PMID: 27486927] 
[94] 
Mortier, J.; Dhakal, P.; Volkamer, A. Truly target-focused pharmacophore modeling: a novel tool for mapping intermolecular surfaces. Molecules,  2018, 23(8), 23.
[http://dx.doi.org/10.3390/molecules23081959] [PMID: 30082611] 
[95] 
Hu, B.; Lill, M.A. Exploring the potential of protein-based pharmacophore models in ligand pose prediction and ranking. J. Chem. Inf. Model.,  2013, 53(5), 1179-1190.
[http://dx.doi.org/10.1021/ci400143r] [PMID: 23621564] 
[96] 
Zhang, W.; Pei, J.; Lai, L. Computational multitarget drug design. J. Chem. Inf. Model.,  2017, 57(3), 403-412.
[http://dx.doi.org/10.1021/acs.jcim.6b00491] [PMID: 28166637] 
[97] 
Geppert, H.; Vogt, M.; Bajorath, J. Current trends in ligand-based virtual screening: molecular representations, data mining methods, new application areas, and performance evaluation. J. Chem. Inf. Model.,  2010, 50(2), 205-216.
[http://dx.doi.org/10.1021/ci900419k] [PMID: 20088575] 
[98] 
Drwal, M.N.; Griffith, R. Combination of ligand- and structure-based methods in virtual screening. Drug Discov. Today. Technol.,  2013, 10(3), e395-e401.
[http://dx.doi.org/10.1016/j.ddtec.2013.02.002] [PMID: 24050136] 
[99] 
Yang, S.Y. Pharmacophore modeling and applications in drug discovery: challenges and recent advances. Drug Discov. Today,  2010, 15(11-12), 444-450.
[http://dx.doi.org/10.1016/j.drudis.2010.03.013] [PMID: 20362693] 
[100] 
Neves, B.J.; Braga, R.C.; Melo-Filho, C.C.; Moreira-Filho, J.T.; Muratov, E.N.; Andrade, C.H. QSAR-based virtual screening: advances and applications in drug discovery. Front. Pharmacol.,  2018, 9, 1275.
[http://dx.doi.org/10.3389/fphar.2018.01275] [PMID: 30524275] 
[101] 
Prado-Prado, F.J.; Uriarte, E.; Borges, F.; González-Díaz, H. Multi-target spectral moments for QSAR and complex networks study of antibacterial drugs. Eur. J. Med. Chem.,  2009, 44(11), 4516-4521.
[http://dx.doi.org/10.1016/j.ejmech.2009.06.018] [PMID: 19631422] 
[102] 
Prado-Prado, F.J.; González-Díaz, H.; de la Vega, O.M.; Ubeira, F.M.; Chou, K.C. Unified QSAR approach to antimicrobials. Part 3: pirst multi-tasking QSAR model for input-coded prediction, structural back-projection, and complex networks clustering of antiprotozoal compounds. Bioorg. Med. Chem.,  2008, 16(11), 5871-5880.
[http://dx.doi.org/10.1016/j.bmc.2008.04.068] [PMID: 18485714] 
[103] 
Prado-Prado, F.J.; Martinez de la Vega, O.; Uriarte, E.; Ubeira, F.M.; Chou, K.C.; González-Díaz, H. Unified QSAR approach to antimicrobials. 4. Multi-target QSAR modeling and comparative multi-distance study of the giant components of antiviral drug-drug complex networks. Bioorg. Med. Chem.,  2009, 17(2), 569-575.
[http://dx.doi.org/10.1016/j.bmc.2008.11.075] [PMID: 19112024] 
[104] 
Gupta, R.; Srivastava, D.; Sahu, M.; Tiwari, S.; Ambasta, R.K.; Kumar, P. Artificial intelligence to deep learning: machine intelligence approach for drug discovery. Mol. Divers.,  2021, 25(3), 1315-1360.
[http://dx.doi.org/10.1007/s11030-021-10217-3] [PMID: 33844136] 
[105] 
Ma, J.; Sheridan, R.P.; Liaw, A.; Dahl, G.E.; Svetnik, V. Deep neural nets as a method for quantitative structure-activity relationships. J. Chem. Inf. Model.,  2015, 55(2), 263-274.
[http://dx.doi.org/10.1021/ci500747n] [PMID: 25635324] 
[106] 
Xu, Y.; Ma, J.; Liaw, A.; Sheridan, R.P.; Svetnik, V. Demystifying multitask deep neural networks for quantitative structure-activity relationships. J. Chem. Inf. Model.,  2017, 57(10), 2490-2504.
[http://dx.doi.org/10.1021/acs.jcim.7b00087] [PMID: 28872869] 
[107] 
Tan, X.; Jiang, X.; He, Y.; Zhong, F.; Li, X.; Xiong, Z.; Li, Z.; Liu, X.; Cui, C.; Zhao, Q.; Xie, Y.; Yang, F.; Wu, C.; Shen, J.; Zheng, M.; Wang, Z.; Jiang, H. Automated design and optimization of multitarget schizophrenia drug candidates by deep learning. Eur. J. Med. Chem.,  2020, 204, 112572.
[http://dx.doi.org/10.1016/j.ejmech.2020.112572] [PMID: 32711293] 
[108] 
Feldmann, C.; Yonchev, D.; Bajorath, J. Analysis of biological screening Compounds with single- or multi-target activity via diagnostic machine learning. Biomolecules,  2020, 10(12), 1-17.
[http://dx.doi.org/10.3390/biom10121605] [PMID: 33260876] 
[109] 
Wei, D.; Jiang, X.; Zhou, L.; Chen, J.; Chen, Z.; He, C.; Yang, K.; Liu, Y.; Pei, J.; Lai, L. Discovery of multitarget inhibitors by combining molecular docking with common pharmacophore matching. J. Med. Chem.,  2008, 51(24), 7882-7888.
[http://dx.doi.org/10.1021/jm8010096] [PMID: 19090779] 
[110] 
Chen, J.; Lai, L. Pocket v.2: further developments on receptor-based pharmacophore modeling. J. Chem. Inf. Model.,  2006, 46(6), 2684-2691.
[http://dx.doi.org/10.1021/ci600246s] [PMID: 17125208] 
[111] 
Wang, G.; Zhao, Y.; Liu, Y.; Sun, D.; Zhen, Y.; Liu, J.; Fu, L.; Zhang, L.; Ouyang, L. Discovery of a novel dual-target inhibitor of erk1 and erk5 that induces regulated cell death to overcome compensatory mechanism in specific tumor types. J. Med. Chem.,  2020, 63(8), 3976-3995.
[http://dx.doi.org/10.1021/acs.jmedchem.9b01896] [PMID: 32078308] 
[112] 
Diller, D.J.; Merz, K.M. Jr. High throughput docking for library design and library prioritization. Proteins,  2001, 43(2), 113-124.
[http://dx.doi.org/10.1002/1097-0134(20010501)43:2<113::AID-PROT1023>3.0.CO;2-T] [PMID: 11276081] 
[113] 
Moser, D.; Wisniewska, J.M.; Hahn, S.; Achenbach, J.; Buscató, El.; Klingler, F.M.; Hofmann, B.; Steinhilber, D.; Proschak, E. Dual-target virtual screening by pharmacophore elucidation and molecular shape filtering. ACS Med. Chem. Lett.,  2012, 3(2), 155-158.
[http://dx.doi.org/10.1021/ml200286e] [PMID: 24900445] 
[114] 
Werz, O.; Steinhilber, D. Therapeutic options for 5-lipoxygenase inhibitors. Pharmacol. Ther.,  2006, 112(3), 701-718.
[http://dx.doi.org/10.1016/j.pharmthera.2006.05.009] [PMID: 16837050] 
[115] 
Imig, J.D.; Hammock, B.D. Soluble epoxide hydrolase as a therapeutic target for cardiovascular diseases. Nat. Rev. Drug Discov.,  2009, 8(10), 794-805.
[http://dx.doi.org/10.1038/nrd2875] [PMID: 19794443] 
[116] 
Sang, Z.; Wang, K.; Wang, H.; Wang, H.; Ma, Q.; Han, X.; Ye, M.; Yu, L.; Liu, W. Design, synthesis and biological evaluation of 2-acetyl-5-O-(amino-alkyl)phenol derivatives as multifunctional agents for the treatment of Alzheimer’s disease. Bioorg. Med. Chem. Lett.,  2017, 27(22), 5046-5052.
[http://dx.doi.org/10.1016/j.bmcl.2017.09.057] [PMID: 29033233] 
[117] 
Samochocki, M.; Höffle, A.; Fehrenbacher, A.; Jostock, R.; Ludwig, J.; Christner, C.; Radina, M.; Zerlin, M.; Ullmer, C.; Pereira, E.F.R.; Lübbert, H.; Albuquerque, E.X.; Maelicke, A. Galantamine is an allosterically potentiating ligand of neuronal nicotinic but not of muscarinic acetylcholine receptors. J. Pharmacol. Exp. Ther.,  2003, 305(3), 1024-1036.
[http://dx.doi.org/10.1124/jpet.102.045773] [PMID: 12649296] 
[118] 
Texidó, L.; Ros, E.; Martín-Satué, M.; López, S.; Aleu, J.; Marsal, J.; Solsona, C. Effect of galantamine on the human alpha7 neuronal nicotinic acetylcholine receptor, the Torpedo nicotinic acetylcholine receptor and spontaneous cholinergic synaptic activity. Br. J. Pharmacol.,  2005, 145(5), 672-678.
[http://dx.doi.org/10.1038/sj.bjp.0706221] [PMID: 15834443] 
[119] 
Kowal, N.M.; Indurthi, D.C.; Ahring, P.K.; Chebib, M.; Olafsdottir, E.S.; Balle, T. Novel approach for the search for chemical scaffolds with dual activity with acetylcholinesterase and the α7 nicotinic acetylcholine receptor-a perspective for the treatment of neurodegenerative disorders. Molecules,  2019, 24(3), 24.
[http://dx.doi.org/10.3390/molecules24030446] [PMID: 30691196] 
[120] 
De Simone, A.; Russo, D.; Ruda, G.F.; Micoli, A.; Ferraro, M.; Di Martino, R.M.C.; Ottonello, G.; Summa, M.; Armirotti, A.; Bandiera, T.; Cavalli, A.; Bottegoni, G. Design, synthesis, structure-activity relationship studies, and three-dimensional quantitative structure-activity relationship (3d-qsar) modeling of a series of o-biphenyl carbamates as dual modulators of dopamine d3 receptor and fatty acid amide hydrolase. J. Med. Chem.,  2017, 60(6), 2287-2304.
[http://dx.doi.org/10.1021/acs.jmedchem.6b01578] [PMID: 28182408] 
[121] 
Ferraro, M.; Decherchi, S.; De Simone, A.; Recanatini, M.; Cavalli, A.; Bottegoni, G. Multi-target dopamine D3 receptor modulators: actionable knowledge for drug design from molecular dynamics and machine learning. Eur. J. Med. Chem.,  2020, 188, 111975.
[http://dx.doi.org/10.1016/j.ejmech.2019.111975] [PMID: 31940507] 
Cite As
Current Topics in Medicinal Chemistry

Molecular Modeling Techniques Applied to the Design of Multitarget Drugs: Methods and Applications

Abstract

Graphical Abstract
