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Mini-Reviews in Medicinal Chemistry

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ISSN (Print): 1389-5575
ISSN (Online): 1875-5607

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Review Article

The QSAR Paradigm in Fragment-Based Drug Discovery: From the Virtual Generation of Target Inhibitors to Multi-Scale Modeling

Author(s): Valeria V. Kleandrova and Alejandro Speck-Planche*

Volume 20, Issue 14, 2020

Page: [1357 - 1374] Pages: 18

DOI: 10.2174/1389557520666200204123156

Price: $65

Abstract

Fragment-Based Drug Design (FBDD) has established itself as a promising approach in modern drug discovery, accelerating and improving lead optimization, while playing a crucial role in diminishing the high attrition rates at all stages in the drug development process. On the other hand, FBDD has benefited from the application of computational methodologies, where the models derived from the Quantitative Structure-Activity Relationships (QSAR) have become consolidated tools. This mini-review focuses on the evolution and main applications of the QSAR paradigm in the context of FBDD in the last five years. This report places particular emphasis on the QSAR models derived from fragment-based topological approaches to extract physicochemical and/or structural information, allowing to design potentially novel mono- or multi-target inhibitors from relatively large and heterogeneous databases. Here, we also discuss the need to apply multi-scale modeling, to exemplify how different datasets based on target inhibition can be simultaneously integrated and predicted together with other relevant endpoints such as the biological activity against non-biomolecular targets, as well as in vitro and in vivo toxicity and pharmacokinetic properties. In this context, seminal papers are briefly analyzed. As huge amounts of data continue to accumulate in the domains of the chemical, biological and biomedical sciences, it has become clear that drug discovery must be viewed as a multi-scale optimization process. An ideal multi-scale approach should integrate diverse chemical and biological data and also serve as a knowledge generator, enabling the design of potentially optimal chemicals that may become therapeutic agents.

Keywords: Artificial neural network, docking, FBDD, molecular fragment, multi-scale model, pseudo-linear equation, QSAR.

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[1]
Zartler, E.R.; Shapiro, M.J. Fragment-based drug discovery: A practical approach; JohnWiley & Sons, Ltd: Chichester, West Sussex, 2008.
[http://dx.doi.org/10.1002/9780470721551]
[2]
Erlanson, D.A.; Fesik, S.W.; Hubbard, R.E.; Jahnke, W.; Jhoti, H. Twenty years on: The impact of fragments on drug discovery. Nat. Rev. Drug Discov., 2016, 15(9), 605-619.
[http://dx.doi.org/10.1038/nrd.2016.109] [PMID: 27417849]
[3]
Renaud, J.P.; Chung, C.W.; Danielson, U.H.; Egner, U.; Hennig, M.; Hubbard, R.E.; Nar, H. Biophysics in drug discovery: Impact, challenges and opportunities. Nat. Rev. Drug Discov., 2016, 15(10), 679-698.
[http://dx.doi.org/10.1038/nrd.2016.123] [PMID: 27516170]
[4]
Johnson, C.N.; Erlanson, D.A.; Murray, C.W.; Rees, D.C. Fragment-to-Lead medicinal chemistry publications in 2015. J. Med. Chem., 2017, 60(1), 89-99.
[http://dx.doi.org/10.1021/acs.jmedchem.6b01123] [PMID: 27739691]
[5]
Erlanson, D. http://practicalfragments.blogspot.com/2018/10/fragments-in-clinic-2018-edition.html2019.
[6]
Jahnke, W.; Erlanson, D.A. Fragment-based approaches in drug discovery, Wiley-VCH Verlag GmbH Co. KGaA: Weinheim,Germany. 2006.
[http://dx.doi.org/10.1002/3527608761]
[7]
DiMasi, J.A.; Grabowski, H.G.; Hansen, R.W. Innovation in the pharmaceutical industry: New estimates of R&D costs. J. Health Econ., 2016, 47, 20-33.
[http://dx.doi.org/10.1016/j.jhealeco.2016.01.012] [PMID: 26928437]
[8]
Lamoree, B.; Hubbard, R.E. Current perspectives in fragment based lead discovery (FBLD). Essays Biochem., 2017, 61(5), 453-464.
[http://dx.doi.org/10.1042/EBC20170028] [PMID: 29118093]
[9]
Najjar, A.; Platzer, C.; Luft, A.; Aßmann, C.A.; Elghazawy, N.H.; Erdmann, F.; Sippl, W.; Schmidt, M. Computer-aided design, synthesis and biological characterization of novel inhibitors for PKMYT1. Eur. J. Med. Chem., 2019, 161, 479-492.
[http://dx.doi.org/10.1016/j.ejmech.2018.10.050] [PMID: 30388464]
[10]
Kanan, T.; Kanan, D.; Erol, I.; Yazdi, S.; Stein, M.; Durdagi, S. Targeting the NF-κB/IκBα complex via fragment based E-Pharmacophore virtual screening and binary QSAR models. J. Mol. Graph. Model., 2019, 86, 264-277.
[http://dx.doi.org/10.1016/j.jmgm.2018.09.014] [PMID: 30415122]
[11]
Zaka, M.; Abbasi, B.H.; Durdagi, S. Proposing novel TNFα direct inhibitor Scaffolds using fragment-docking based e-pharmacophore modeling and binary QSAR-based virtual screening protocols pipeline. J. Mol. Graph. Model., 2018, 85, 111-121.
[http://dx.doi.org/10.1016/j.jmgm.2018.07.007] [PMID: 30149308]
[12]
Bian, Y.; Feng, Z.; Yang, P.; Xie, X.Q. Integrated in silico fragment-based drug design: Case study with allosteric modulators on metabotropic glutamate receptor 5. AAPS J., 2017, 19(4), 1235-1248.
[http://dx.doi.org/10.1208/s12248-017-0093-5] [PMID: 28560482]
[13]
Putin, E.; Asadulaev, A.; Ivanenkov, Y.; Aladinskiy, V.; Sanchez-Lengeling, B.; Aspuru-Guzik, A.; Zhavoronkov, A. Reinforced adversarial neural computer for de novo molecular Design. J. Chem. Inf. Model., 2018, 58(6), 1194-1204.
[http://dx.doi.org/10.1021/acs.jcim.7b00690] [PMID: 29762023]
[14]
Gómez-Bombarelli, R.; Wei, J.N.; Duvenaud, D.; Hernández-Lobato, J.M.; Sánchez-Lengeling, B.; Sheberla, D.; Aguilera-Iparraguirre, J.; Hirzel, T.D.; Adams, R.P.; Aspuru-Guzik, A. Automatic chemical design using a data-driven continuous representation of molecules. ACS Cent. Sci., 2018, 4(2), 268-276.
[http://dx.doi.org/10.1021/acscentsci.7b00572] [PMID: 29532027]
[15]
Segler, M.H.S.; Kogej, T.; Tyrchan, C.; Waller, M.P. Generating focused molecule libraries for drug discovery with recurrent neural networks. ACS Cent. Sci., 2018, 4(1), 120-131.
[http://dx.doi.org/10.1021/acscentsci.7b00512] [PMID: 29392184]
[16]
Kadurin, A.; Nikolenko, S.; Khrabrov, K.; Aliper, A.; Zhavoronkov, A. druGAN: An advanced generative adversarial autoencoder model for de novo generation of new molecules with desired molecular properties in silico. Mol. Pharm., 2017, 14(9), 3098-3104.
[http://dx.doi.org/10.1021/acs.molpharmaceut.7b00346] [PMID: 28703000]
[17]
Di Paolo, T.; Kier, L.B. Molecular connectivity and structure activity relationship of general anesthetics. Mol. Pharmacol., 1977, 13(1), 31-37.
[PMID: 834185]
[18]
Prado-Prado, F.J.; García-Mera, X.; González-Díaz, H. Multi-target spectral moment QSAR versus ANN for antiparasitic drugs against different parasite species. Bioorg. Med. Chem., 2010, 18(6), 2225-2231.
[http://dx.doi.org/10.1016/j.bmc.2010.01.068] [PMID: 20185316]
[19]
Speck-Planche, A.; Luan, F.; Cordeiro, M.N.D.S. Role of ligand based drug design methodologies toward the discovery of new anti- Alzheimer agents: Futures perspectives in fragment-based ligand design. Curr. Med. Chem., 2012, 19(11), 1635-1645.
[http://dx.doi.org/10.2174/092986712799945058] [PMID: 22376033]
[20]
Speck-Planche, A.; Luan, F.; Cordeiro, M.N.D.S. Discovery of anti-Alzheimer agents: Current ligand-based approaches toward the design of acetylcholinesterase inhibitors. Mini Rev. Med. Chem., 2012, 12(6), 583-591.
[http://dx.doi.org/10.2174/138955712800493744] [PMID: 22587771]
[21]
Hansch, C.; Leo, A. Exploring QSAR: Fundamentals and applications in Chemistry and Biology; American Chemical Society: Washington, DC, 1995.
[22]
Todeschini, R.; Consonni, V. Handbook of Molecular Descriptors; WILEY-VCH Verlag GmbH: Weinheim, New York, Chichester, Brisbane, Singapore, Toronto, 2000.
[http://dx.doi.org/10.1002/9783527613106]
[23]
van de Waterbeemd, H. Chemometric methods in molecular design; VCH Publishers: Weinheim, New York, Basel, Cambridge, Tokyo, 1995.
[http://dx.doi.org/10.1002/9783527615452]
[24]
Lo, Y.C.; Rensi, S.E.; Torng, W.; Altman, R.B. Machine learning in chemoinformatics and drug discovery. Drug Discov. Today, 2018, 23(8), 1538-1546.
[http://dx.doi.org/10.1016/j.drudis.2018.05.010] [PMID: 29750902]
[25]
Baskin, I.; Varnek, A. Fragment descriptors in SAR/QSAR/QSPR studies, molecular similarity analysis and in virtual screening. Chemoinformatics approaches to virtual screening; Varnek, A.; Tropsha, A., Eds.; Royal Society of Chemistry: Cambridge, 2008, pp. 1-43.
[http://dx.doi.org/10.1039/9781847558879-00001]
[26]
Amaro, R.E.; Baudry, J.; Chodera, J.; Demir, Ö.; McCammon, J.A.; Miao, Y.; Smith, J.C. Ensemble docking in drug discovery. Biophys. J., 2018, 114(10), 2271-2278.
[http://dx.doi.org/10.1016/j.bpj.2018.02.038] [PMID: 29606412]
[27]
Velazquez, H.A.; Riccardi, D.; Xiao, Z.; Quarles, L.D.; Yates, C.R.; Baudry, J.; Smith, J.C. Ensemble docking to difficult targets in early-stage drug discovery: Methodology and application to fibroblast growth factor 23. Chem. Biol. Drug Des., 2018, 91(2), 491-504.
[http://dx.doi.org/10.1111/cbdd.13110] [PMID: 28944571]
[28]
Lee, A.; Lee, K.; Kim, D. Using reverse docking for target identification and its applications for drug discovery. Expert Opin. Drug Discov., 2016, 11(7), 707-715.
[http://dx.doi.org/10.1080/17460441.2016.1190706] [PMID: 27186904]
[29]
Wong, C.F. Flexible receptor docking for drug discovery. Expert Opin. Drug Discov., 2015, 10(11), 1189-1200.
[http://dx.doi.org/10.1517/17460441.2015.1078308] [PMID: 26313123]
[30]
Liu, X.; Shi, D.; Zhou, S.; Liu, H.; Liu, H.; Yao, X. Molecular dynamics simulations and novel drug discovery. Expert Opin. Drug Discov., 2018, 13(1), 23-37.
[http://dx.doi.org/10.1080/17460441.2018.1403419] [PMID: 29139324]
[31]
Ganesan, A.; Coote, M.L.; Barakat, K. Molecular dynamics-driven drug discovery: Leaping forward with confidence. Drug Discov. Today, 2017, 22(2), 249-269.
[http://dx.doi.org/10.1016/j.drudis.2016.11.001] [PMID: 27890821]
[32]
Aci-Sèche, S.; Ziada, S.; Braka, A.; Arora, R.; Bonnet, P. Advanced molecular dynamics simulation methods for kinase drug discovery. Future Med. Chem., 2016, 8(5), 545-566.
[http://dx.doi.org/10.4155/fmc.16.9] [PMID: 27054816]
[33]
Garland, S.L.; Gloriam, D.E. A ligand’s view of target similarity: Chemogenomic binding site-directed techniques for drug discovery. Curr. Top. Med. Chem., 2011, 11(15), 1872-1881.
[http://dx.doi.org/10.2174/156802611796391276] [PMID: 21470171]
[34]
Hart, T.; Xie, L. Providing data science support for systems pharmacology and its implications to drug discovery. Expert Opin. Drug Discov., 2016, 11(3), 241-256.
[http://dx.doi.org/10.1517/17460441.2016.1135126] [PMID: 26689499]
[35]
Lauria, A.; Bonsignore, R.; Bartolotta, R.; Perricone, U.; Martorana, A.; Gentile, C. Drugs polypharmacology by in silico methods: New opportunities in drug discovery. Curr. Pharm. Des., 2016, 22(21), 3073-3081.
[http://dx.doi.org/10.2174/1381612822666160224142323] [PMID: 26907944]
[36]
Veselinović, A.M.; Toropov, A.; Toropova, A.; Stanković Đorđević, D.; Veselinović, J.B. Design and development of novel antibiotics based on FtsZ inhibition – in silico studies. New J. Chem., 2018, 42, 10976-10982.
[http://dx.doi.org/10.1039/C8NJ01034J]
[37]
Halder, A.K. Finding the structural requirements of diverse HIV-1 protease inhibitors using multiple QSAR modelling for lead identification. SAR QSAR Environ. Res., 2018, 29(11), 911-933.
[http://dx.doi.org/10.1080/1062936X.2018.1529702] [PMID: 30332922]
[38]
Silva, D.G.; Rocha, J.R.; Sartori, G.R.; Montanari, C.A. Highly predictive hologram QSAR models of nitrile-containing cruzain inhibitors. J. Biomol. Struct. Dyn., 2017, 35(15), 3232-3249.
[http://dx.doi.org/10.1080/07391102.2016.1252282] [PMID: 27809673]
[39]
Goyal, S.; Dhanjal, J.K.; Tyagi, C.; Goyal, M.; Grover, A. Novel fragment-based QSAR modeling and combinatorial design of pyrazole-derived CRK3 inhibitors as potent antileishmanials. Chem. Biol. Drug Des., 2014, 84(1), 54-62.
[http://dx.doi.org/10.1111/cbdd.12290] [PMID: 24447365]
[40]
Joshi, K.; Goyal, S.; Grover, S.; Jamal, S.; Singh, A.; Dhar, P.; Grover, A. Novel group-based QSAR and combinatorial design of CK-1δ inhibitors as neuroprotective agents. BMC Bioinformat., 2016, 17(Suppl. 19), 515.
[http://dx.doi.org/10.1186/s12859-016-1379-9] [PMID: 28155653]
[41]
Sinha, S.; Tyagi, C.; Goyal, S.; Jamal, S.; Somvanshi, P.; Grover, A. Fragment based G-QSAR and molecular dynamics based mechanistic simulations into hydroxamic-based HDAC inhibitors against spinocerebellar ataxia. J. Biomol. Struct. Dyn., 2016, 34(10), 2281-2295.
[http://dx.doi.org/10.1080/07391102.2015.1113386] [PMID: 26510381]
[42]
Goyal, M.; Dhanjal, J.K.; Goyal, S.; Tyagi, C.; Hamid, R.; Grover, A. Development of dual inhibitors against Alzheimer’s disease using fragment-based QSAR and molecular docking. BioMed Res. Int., 2014, 2014, 979606.
[http://dx.doi.org/10.1155/2014/979606] [PMID: 25019089]
[43]
Yang, X.L.; Zhou, Y.; Liu, X.L. Fragment-based hologram QSAR studies on a series of 2,4-Dioxopyrimidine-1-Carboxamides as highly potent inhibitors of acid Ceramidase. Iran. J. Pharm. Res., 2016, 15(Suppl.), 139-148.
[PMID: 28058055]
[44]
Goyal, S.; Grover, S.; Dhanjal, J.K.; Tyagi, C.; Goyal, M.; Grover, A. Group-based QSAR and molecular dynamics mechanistic analysis revealing the mode of action of novel piperidinone derived protein-protein inhibitors of p53-MDM2. J. Mol. Graph. Model., 2014, 51, 64-72.
[http://dx.doi.org/10.1016/j.jmgm.2014.04.015] [PMID: 24858256]
[45]
Saíz-Urra, L.; Teijeira, M.; Rivero-Buceta, V.; Helguera, A.M.; Celeiro, M.; Terán, M.C.; Besada, P.; Borges, F. Topological sub structural molecular design (TOPS-MODE): A useful tool to explore key fragments of human A3 adenosine receptor ligands. Mol. Divers., 2016, 20(1), 55-76.
[http://dx.doi.org/10.1007/s11030-015-9617-z] [PMID: 26205409]
[46]
Wang, J.; Drake, L.; Sajjadi, F.; Firestein, G.S.; Mullane, K.M.; Bullough, D.A. Dual activation of adenosine A1 and A3 receptors mediates preconditioning of isolated cardiac myocytes. Eur. J. Pharmacol., 1997, 320(2-3), 241-248.
[http://dx.doi.org/10.1016/S0014-2999(96)00901-6] [PMID: 9059860]
[47]
Kolachala, V.L.; Bajaj, R.; Chalasani, M.; Sitaraman, S.V. Purinergic receptors in gastrointestinal inflammation. Am. J. Physiol. Gastrointest. Liver Physiol., 2008, 294(2), G401-G410.
[http://dx.doi.org/10.1152/ajpgi.00454.2007] [PMID: 18063703]
[48]
Wan, T.C.; Ge, Z.D.; Tampo, A.; Mio, Y.; Bienengraeber, M.W.; Tracey, W.R.; Gross, G.J.; Kwok, W.M.; Auchampach, J.A. The A3 adenosine receptor agonist CP-532,903 [N6-(2,5-dichlorobenzyl)-3′-aminoadenosine-5′-N-methylcarboxamide] protects against myocardial ischemia/reperfusion injury via the sarcolemmal ATP-sensitive potassium channel. J. Pharmacol. Exp. Ther., 2008, 324(1), 234-243.
[http://dx.doi.org/10.1124/jpet.107.127480] [PMID: 17906066]
[49]
Madi, L.; Ochaion, A.; Rath-Wolfson, L.; Bar-Yehuda, S.; Erlanger, A.; Ohana, G.; Harish, A.; Merimski, O.; Barer, F.; Fishman, P. The A3 adenosine receptor is highly expressed in tumor versus normal cells: Potential target for tumor growth inhibition. Clin. Cancer Res., 2004, 10(13), 4472-4479.
[http://dx.doi.org/10.1158/1078-0432.CCR-03-0651] [PMID: 15240539]
[50]
Estrada, E.; Vilar, S.; Uriarte, E.; Gutierrez, Y. In silico studies toward the discovery of new anti-HIV nucleoside compounds with the use of TOPS-MODE and 2D/3D connectivity indices. 1. Pyrimidyl derivatives. J. Chem. Inf. Comput. Sci., 2002, 42(5), 1194-1203.
[http://dx.doi.org/10.1021/ci0255331] [PMID: 12377009]
[51]
Pérez González, M.; Gonzalez Díaz, H.; Molina Ruiz, R.; Cabrera, M.A.; Ramos de Armas, R. TOPS-MODE based QSARs derived from heterogeneous series of compounds. Applications to the design of new herbicides. J. Chem. Inf. Comput. Sci., 2003, 43(4), 1192-1199.
[http://dx.doi.org/10.1021/ci034039+] [PMID: 12870911]
[52]
Estrada, E.; Patlewicz, G.; Gutierrez, Y. From knowledge generation to knowledge archive. A general strategy using TOPS-MODE with DEREK to formulate new alerts for skin sensitization. J. Chem. Inf. Comput. Sci., 2004, 44(2), 688-698.
[http://dx.doi.org/10.1021/ci0342425] [PMID: 15032551]
[53]
Planche, A.S.; Scotti, M.T.; López, A.G.; de Paulo Emerenciano, V.; Pérez, E.M.; Uriarte, E. Design of novel antituberculosis compounds using graph-theoretical and substructural approaches. Mol. Divers., 2009, 13(4), 445-458.
[http://dx.doi.org/10.1007/s11030-009-9129-9] [PMID: 19340599]
[54]
Stolk, R.P.; Rosmalen, J.G.; Postma, D.S.; de Boer, R.A.; Navis, G.; Slaets, J.P.; Ormel, J.; Wolffenbuttel, B.H. Universal risk factors for multifactorial diseases: LifeLines: A three-generation population-based study. Eur. J. Epidemiol., 2008, 23(1), 67-74.
[http://dx.doi.org/10.1007/s10654-007-9204-4] [PMID: 18075776]
[55]
Friedman, R. Drug resistance in cancer: Molecular evolution and compensatory proliferation. Oncotarget, 2016, 7(11), 11746-11755.
[http://dx.doi.org/10.18632/oncotarget.7459] [PMID: 26909596]
[56]
Vasoo, S.; Barreto, J.N.; Tosh, P.K. Emerging issues in gram negative bacterial resistance: An update for the practicing clinician. Mayo Clin. Proc., 2015, 90(3), 395-403.
[http://dx.doi.org/10.1016/j.mayocp.2014.12.002] [PMID: 25744116]
[57]
Holohan, C.; Van Schaeybroeck, S.; Longley, D.B.; Johnston, P.G. Cancer drug resistance: An evolving paradigm. Nat. Rev. Cancer, 2013, 13(10), 714-726.
[http://dx.doi.org/10.1038/nrc3599] [PMID: 24060863]
[58]
Viña, D.; Uriarte, E.; Orallo, F.; González-Díaz, H. Alignment-free prediction of a drug-target complex network based on parameters of drug connectivity and protein sequence of receptors. Mol. Pharm., 2009, 6(3), 825-835.
[http://dx.doi.org/10.1021/mp800102c] [PMID: 19281186]
[59]
González-Díaz, H.; Prado-Prado, F.; García-Mera, X.; Alonso, N.; Abeijón, P.; Caamaño, O.; Yáñez, M.; Munteanu, C.R.; Pazos, A.; Dea-Ayuela, M.A.; Gómez-Muñoz, M.T.; Garijo, M.M.; Sansano, J.; Ubeira, F.M. MIND-BEST: Web server for drugs and target discovery; design, synthesis, and assay of MAO-B inhibitors and theoretical-experimental study of G3PDH protein from Trichomonas gallinae. J. Proteome Res., 2011, 10(4), 1698-1718.
[http://dx.doi.org/10.1021/pr101009e] [PMID: 21184613]
[60]
González-Díaz, H.; Prado-Prado, F.; Sobarzo-Sánchez, E.; Haddad, M.; Maurel Chevalley, S.; Valentin, A.; Quetin-Leclercq, J.; Dea-Ayuela, M.A.; Teresa Gomez-Muños, M.; Munteanu, C.R.; José Torres-Labandeira, J.; García-Mera, X.; Tapia, R.A.; Ubeira, F.M.N.L.NL MIND-BEST: A web server for ligands and proteins discovery--theoretic-experimental study of proteins of Giardia lamblia and new compounds active against Plasmodium falciparum. J. Theor. Biol., 2011, 276(1), 229-249.
[http://dx.doi.org/10.1016/j.jtbi.2011.01.010] [PMID: 21277861]
[61]
Prado-Prado, F.; García-Mera, X.; Abeijón, P.; Alonso, N.; Caamaño, O.; Yáñez, M.; Gárate, T.; Mezo, M.; González-Warleta, M.; Muiño, L.; Ubeira, F.M.; González-Díaz, H. Using entropy of drug and protein graphs to predict FDA drug-target network: Theoretic experimental study of MAO inhibitors and hemoglobin peptides from Fasciola hepatica. Eur. J. Med. Chem., 2011, 46(4), 1074-1094.
[http://dx.doi.org/10.1016/j.ejmech.2011.01.023] [PMID: 21315497]
[62]
Prado-Prado, F.; García-Mera, X.; Escobar, M.; Alonso, N.; Caamaño, O.; Yañez, M.; González-Díaz, H. 3D MI-DRAGON: New model for the reconstruction of US FDA drug- target network and theoretical-experimental studies of inhibitors of rasagiline derivatives for AChE. Curr. Top. Med. Chem., 2012, 12(16), 1843-1865.
[http://dx.doi.org/10.2174/1568026611209061843] [PMID: 23030618]
[63]
Speck-Planche, A.; Cordeiro, M.N.D.S. Application of bioinformatics for the search of novel anti-viral therapies: Rational design of anti-herpes agents. Curr. Bioinform., 2011, 6, 81-93.
[http://dx.doi.org/10.2174/157489311795222392]
[64]
Speck-Planche, A.; Kleandrova, V.V.; Luan, F.; Cordeiro, M.N.D.S. In silico discovery and virtual screening of multi-target inhibitors for proteins in Mycobacterium tuberculosis. Comb. Chem. High Throughput Screen., 2012, 15(8), 666-673.
[http://dx.doi.org/10.2174/138620712802650487] [PMID: 22452349]
[65]
Speck-Planche, A.; Kleandrova, V.V.; Scotti, M.T.; Cordeiro, M.N.D.S. 3D-QSAR methodologies and molecular modeling in bioinformatics for the search of novel anti-HIV therapies: Rational design of entry inhibitors. Curr. Bioinform., 2013, 8, 452-464.
[http://dx.doi.org/10.2174/1574893611308040007]
[66]
Marzaro, G.; Chilin, A.; Guiotto, A.; Uriarte, E.; Brun, P.; Castagliuolo, I.; Tonus, F.; González-Díaz, H. Using the TOPS-MODE approach to fit multi-target QSAR models for tyrosine kinases inhibitors. Eur. J. Med. Chem., 2011, 46(6), 2185-2192.
[http://dx.doi.org/10.1016/j.ejmech.2011.02.072] [PMID: 21447431]
[67]
Speck-Planche, A.; Kleandrova, V.V. In silico design of multi target inhibitors for C-C chemokine receptors using substructural descriptors. Mol. Divers., 2012, 16(1), 183-191.
[http://dx.doi.org/10.1007/s11030-011-9337-y] [PMID: 22020812]
[68]
Speck-Planche, A.; Kleandrova, V.V.; Luan, F.; Cordeiro, M.N.D.S. Multi-target inhibitors for proteins associated with Alzheimer: In silico discovery using fragment-based descriptors. Curr. Alzheimer Res., 2013, 10(2), 117-124.
[http://dx.doi.org/10.2174/1567205011310020001] [PMID: 22515494]
[69]
García, I.; Fall, Y.; Gómez, G.; González-Díaz, H. First computational chemistry multi-target model for anti-Alzheimer, anti-parasitic, anti-fungi, and anti-bacterial activity of GSK-3 inhibitors in vitro, in vivo, and in different cellular lines. Mol. Divers., 2011, 15(2), 561-567.
[http://dx.doi.org/10.1007/s11030-010-9280-3] [PMID: 20931280]
[70]
Speck-Planche, A.; Luan, F.; Cordeiro, M.N.D.S. Abelson tyrosine protein kinase 1 as principal target for drug discovery against leukemias. Role of the current computer-aided drug design methodologies. Curr. Top. Med. Chem., 2012, 12(24), 2745-2762.
[http://dx.doi.org/10.2174/1568026611212240005] [PMID: 23368101]
[71]
Molina, E.; Sobarzo-Sanchez, E.; Speck-Planche, A.; Matos, M.J.; Uriarte, E.; Santana, L.; Yanez, M.; Orallo, F. Monoamino oxidase a: An interesting pharmacological target for the development of multi-target QSAR. Mini Rev. Med. Chem., 2012, 12(10), 947-958.
[http://dx.doi.org/10.2174/138955712802762383] [PMID: 22420572]
[72]
Speck-Planche, A.; Cordeiro, M.N.D.S. Multi-target QSAR approaches for modeling protein inhibitors. Simultaneous prediction of activities against biomacromolecules present in gram-negative bacteria. Curr. Top. Med. Chem., 2015, 15(18), 1801-1813.
[http://dx.doi.org/10.2174/1568026615666150506144814] [PMID: 25961517]
[73]
Baskin, I.I.; Skvortsova, M.I.; Stankevich, I.V.; Zefirov, N.S. On the basis of invariants of labeled molecular graphs. J. Chem. Inf. Comput. Sci., 1995, 35, 527-531.
[http://dx.doi.org/10.1021/ci00025a021]
[74]
Casañola-Martin, G.M.; Le-Thi-Thu, H.; Pérez-Giménez, F.; Marrero-Ponce, Y.; Merino-Sanjuán, M.; Abad, C.; González-Díaz, H. Multi-output model with Box-Jenkins operators of linear indices to predict multi-target inhibitors of ubiquitin-proteasome pathway. Mol. Divers., 2015, 19(2), 347-356.
[http://dx.doi.org/10.1007/s11030-015-9571-9] [PMID: 25754075]
[75]
Gaulton, A.; Bellis, L.J.; Bento, A.P.; Chambers, J.; Davies, M.; Hersey, A.; Light, Y.; McGlinchey, S.; Michalovich, D.; Al-Lazikani, B.; Overington, J.P. ChEMBL: A large-scale bioactivity database for drug discovery. Nucleic Acids Res., 2012, 40(Database issue), D1100-D1107.
[http://dx.doi.org/10.1093/nar/gkr777] [PMID: 21948594]
[76]
Irwin, J.J.; Shoichet, B.K. ZINC--a free database of commercially available compounds for virtual screening. J. Chem. Inf. Model., 2005, 45(1), 177-182.
[http://dx.doi.org/10.1021/ci049714+] [PMID: 15667143]
[77]
Williams, A.J. Chemspider: A platform for crowdsourced collaboration to curate data derived from public compound databases. Collaborative computational technologies for biomedical research; Ekins, S.; Hupcey, M.A.Z; Williams, A.J., Ed.; John Wiley & Sons, Inc.: Hoboken, NJ, 2011, pp. 363-386.
[http://dx.doi.org/10.1002/9781118026038.ch22]
[78]
Lipinski, C.A.; Lombardo, F.; Dominy, B.W.; Feeney, P.J. Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings. Adv. Drug Deliv. Rev., 2001, 46(1-3), 3-26.
[http://dx.doi.org/10.1016/S0169-409X(00)00129-0] [PMID: 11259830]
[79]
Daina, A.; Michielin, O.; Zoete, V. SwissADME: A free web tool to evaluate pharmacokinetics, drug-likeness and medicinal chemistry friendliness of small molecules. Sci. Rep., 2017, 7, 42717.
[http://dx.doi.org/10.1038/srep42717] [PMID: 28256516]
[80]
McGuire, A.; Brown, J.A.; Malone, C.; McLaughlin, R.; Kerin, M.J. Effects of age on the detection and management of breast cancer. Cancers (Basel), 2015, 7(2), 908-929.
[http://dx.doi.org/10.3390/cancers7020815] [PMID: 26010605]
[81]
Lovitt, C.J.; Shelper, T.B.; Avery, V.M. Evaluation of chemotherapeutics in a three-dimensional breast cancer model. J. Cancer Res. Clin. Oncol., 2015, 141(5), 951-959.
[http://dx.doi.org/10.1007/s00432-015-1950-1] [PMID: 25773123]
[82]
Tao, Z.; Shi, A.; Lu, C.; Song, T.; Zhang, Z.; Zhao, J. Breast cancer: Epidemiology and etiology. Cell Biochem. Biophys., 2015, 72(2), 333-338.
[http://dx.doi.org/10.1007/s12013-014-0459-6] [PMID: 25543329]
[83]
Speck-Planche, A.; Cordeiro, M.N.D.S. Fragment-based in silico modeling of multi-target inhibitors against breast cancer-related proteins. Mol. Divers., 2017, 21(3), 511-523.
[http://dx.doi.org/10.1007/s11030-017-9731-1] [PMID: 28194627]
[84]
Zhang, L.; Tan, J.; Han, D.; Zhu, H. From machine learning to deep learning: Progress in machine intelligence for rational drug discovery. Drug Discov. Today, 2017, 22(11), 1680-1685.
[http://dx.doi.org/10.1016/j.drudis.2017.08.010] [PMID: 28881183]
[85]
Zabinsky, R.A.; Mason, G.A.; Queitsch, C.; Jarosz, D.F. It’s not magic - Hsp90 and its effects on genetic and epigenetic variation. Semin. Cell Dev. Biol., 2019, 88, 21-35.
[http://dx.doi.org/10.1016/j.semcdb.2018.05.015] [PMID: 29807130]
[86]
Calderwood, S.K.; Khaleque, M.A.; Sawyer, D.B.; Ciocca, D.R. Heat shock proteins in cancer: Chaperones of tumorigenesis. Trends Biochem. Sci., 2006, 31(3), 164-172.
[http://dx.doi.org/10.1016/j.tibs.2006.01.006] [PMID: 16483782]
[87]
Statsoft-Team. STATISTICA. Data analysis software system, v6.0; Tulsa;, 2001.
[88]
Speck-Planche, A.; Scotti, M.T. BET bromodomain inhibitors: Fragment-based in silico design using multi-target QSAR models. Mol. Divers., 2019, 23(3), 555-572.
[http://dx.doi.org/10.1007/s11030-11018-19890-11038] [PMID: 30421269]
[89]
Liu, Z.; Wang, P.; Chen, H.; Wold, E.A.; Tian, B.; Brasier, A.R.; Zhou, J. Drug discovery targeting bromodomain-containing protein 4. J. Med. Chem., 2017, 60(11), 4533-4558.
[http://dx.doi.org/10.1021/acs.jmedchem.6b01761] [PMID: 28195723]
[90]
Prado-Prado, F.J.; Ubeira, F.M.; Borges, F.; González-Díaz, H. Unified QSAR & network-based computational chemistry approach to antimicrobials. II. Multiple distance and triadic census analysis of antiparasitic drugs complex networks. J. Comput. Chem., 2010, 31(1), 164-173.
[http://dx.doi.org/10.1002/jcc.21292] [PMID: 19421992]
[91]
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]
[92]
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]
[93]
Prado-Prado, F.J.; Borges, F.; Uriarte, E.; Peréz-Montoto, L.G.; González-Díaz, H. Multi-target spectral moment: QSAR for antiviral drugs vs. different viral species. Anal. Chim. Acta, 2009, 651(2), 159-164.
[http://dx.doi.org/10.1016/j.aca.2009.08.022] [PMID: 19782806]
[94]
Prado-Prado, F.J.; Borges, F.; Perez-Montoto, L.G.; González-Díaz, H. Multi-target spectral moment: QSAR for antifungal drugs vs. different fungi species. Eur. J. Med. Chem., 2009, 44(10), 4051-4056.
[http://dx.doi.org/10.1016/j.ejmech.2009.04.040] [PMID: 19467743]
[95]
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: First 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]
[96]
González-Díaz, H.; Prado-Prado, F.J. Unified QSAR and network based computational chemistry approach to antimicrobials, part 1: Multispecies activity models for antifungals. J. Comput. Chem., 2008, 29(4), 656-667.
[http://dx.doi.org/10.1002/jcc.20826] [PMID: 17999385]
[97]
Speck-Planche, A.; Kleandrova, V.V.; Ruso, J.M.; Cordeiro, M.N.D.S. First multitarget chemo-bioinformatic model to enable the discovery of antibacterial peptides against multiple Gram positive pathogens. J. Chem. Inf. Model., 2016, 56(3), 588-598.
[http://dx.doi.org/10.1021/acs.jcim.5b00630] [PMID: 26960000]
[98]
Speck-Planche, A.; Kleandrova, V.V.; Luan, F.; Cordeiro, M.N.D.S. Unified multi-target approach for the rational in silico design of anti-bladder cancer agents. Anticancer. Agents Med. Chem., 2013, 13(5), 791-800.
[http://dx.doi.org/10.2174/1871520611313050013] [PMID: 23272967]
[99]
Speck-Planche, A.; Kleandrova, V.V.; Luan, F.; Cordeiro, M.N.D.S. Chemoinformatics in anti-cancer chemotherapy: Multi target QSAR model for the in silico discovery of anti-breast cancer agents. Eur. J. Pharm. Sci., 2012, 47(1), 273-279.
[http://dx.doi.org/10.1016/j.ejps.2012.04.012] [PMID: 22538055]
[100]
Speck-Planche, A.; Kleandrova, V.V.; Luan, F.; Cordeiro, M.N.D.S. Rational drug design for anti-cancer chemotherapy: Multi-target QSAR models for the in silico discovery of anti-colorectal cancer agents. Bioorg. Med. Chem., 2012, 20(15), 4848-4855.
[http://dx.doi.org/10.1016/j.bmc.2012.05.071] [PMID: 22750007]
[101]
Speck-Planche, A.; Kleandrova, V.V.; Luan, F.; Cordeiro, M.N.D.S. Multi-target drug discovery in anti-cancer therapy: Fragment-based approach toward the design of potent and versatile anti-prostate cancer agents. Bioorg. Med. Chem., 2011, 19(21), 6239-6244.
[http://dx.doi.org/10.1016/j.bmc.2011.09.015] [PMID: 21967806]
[102]
Speck-Planche, A.; Kleandrova, V.V.; Luan, F.; Cordeiro, M.N.D.S. Fragment-based QSAR model toward the selection of versatile anti-sarcoma leads. Eur. J. Med. Chem., 2011, 46(12), 5910-5916.
[http://dx.doi.org/10.1016/j.ejmech.2011.09.055] [PMID: 22030313]
[103]
Speck-Planche, A.; Kleandrova, V.V.; Scotti, M.T. Fragment-based approach for the in silico discovery of multi-target insecticides. Chemom. Intell. Lab. Syst., 2012, 111, 39-45.
[http://dx.doi.org/10.1016/j.chemolab.2011.11.010]
[104]
Speck-Planche, A.; Kleandrova, V.V.; Rojas-Vargas, J.A. QSAR model toward the rational design of new agrochemical fungicides with a defined resistance risk using substructural descriptors. Mol. Divers., 2011, 15(4), 901-909.
[http://dx.doi.org/10.1007/s11030-011-9320-7] [PMID: 21633788]
[105]
Speck-Planche, A.; Cordeiro, M.N.D.S. Multitasking models for quantitative structure-biological effect relationships: Current status and future perspectives to speed up drug discovery. Expert Opin. Drug Discov., 2015, 10(3), 245-256.
[http://dx.doi.org/10.1517/17460441.2015.1006195] [PMID: 25613725]
[106]
Tenorio-Borroto, E.; Peñuelas-Rivas, C.G.; Vásquez-Chagoyán, J.C.; Castañedo, N.; Prado-Prado, F.J.; García-Mera, X.; González-Díaz, H. Model for high-throughput screening of drug immunotoxicity--study of the anti-microbial G1 over peritoneal macrophages using flow cytometry. Eur. J. Med. Chem., 2014, 72, 206-220.
[http://dx.doi.org/10.1016/j.ejmech.2013.08.035] [PMID: 24445280]
[107]
Romero Durán, F.J.; Alonso, N.; Caamaño, O.; García-Mera, X.; Yañez, M.; Prado-Prado, F.J.; González-Díaz, H. Prediction of multi-target networks of neuroprotective compounds with entropy indices and synthesis, assay, and theoretical study of new asymmetric 1,2-rasagiline carbamates. Int. J. Mol. Sci., 2014, 15(9), 17035-17064.
[http://dx.doi.org/10.3390/ijms150917035] [PMID: 25255029]
[108]
Tenorio-Borroto, E.; García-Mera, X.; Peñuelas-Rivas, C.G.; Vásquez-Chagoyán, J.C.; Prado-Prado, F.J.; Castañedo, N.; González-Díaz, H. Entropy model for multiplex drug-target interaction endpoints of drug immunotoxicity. Curr. Top. Med. Chem., 2013, 13(14), 1636-1649.
[http://dx.doi.org/10.2174/15680266113139990114] [PMID: 23889053]
[109]
Luan, F.; Cordeiro, M.N.D.S.; Alonso, N.; García-Mera, X.; Caamaño, O.; Romero-Duran, F.J.; Yañez, M.; González-Díaz, H. TOPS-MODE model of multiplexing neuroprotective effects of drugs and experimental-theoretic study of new 1,3-rasagiline derivatives potentially useful in neurodegenerative diseases. Bioorg. Med. Chem., 2013, 21(7), 1870-1879.
[http://dx.doi.org/10.1016/j.bmc.2013.01.035] [PMID: 23415089]
[110]
Alonso, N.; Caamaño, O.; Romero-Duran, F.J.; Luan, F.D S; Cordeiro, M.N.; Yañez, M.; González-Díaz, H.; García-Mera, X. Model for high-throughput screening of multitarget drugs in chemical neurosciences: Synthesis, assay, and theoretic study of rasagiline carbamates. ACS Chem. Neurosci., 2013, 4(10), 1393-1403.
[http://dx.doi.org/10.1021/cn400111n] [PMID: 23855599]
[111]
Tenorio-Borroto, E.; Peñuelas Rivas, C.G.; Vásquez Chagoyán, J.C.; Castañedo, N.; Prado-Prado, F.J.; García-Mera, X.; González-Díaz, H. ANN multiplexing model of drugs effect on macrophages; theoretical and flow cytometry study on the cytotoxicity of the anti microbial drug G1 in spleen. Bioorg. Med. Chem., 2012, 20(20), 6181-6194.
[http://dx.doi.org/10.1016/j.bmc.2012.07.020] [PMID: 22981917]
[112]
Kleandrova, V.V.; Ruso, J.M.; Speck-Planche, A.; Dias Soeiro Cordeiro, M.N. Enabling the discovery and virtual screening of potent and safe antimicrobial peptides. Simultaneous prediction of antibacterial activity and cytotoxicity. ACS Comb. Sci., 2016, 18(8), 490-498.
[http://dx.doi.org/10.1021/acscombsci.6b00063] [PMID: 27280735]
[113]
Speck-Planche, A.; Cordeiro, M.N.D.S. Enabling virtual screening of potent and safer antimicrobial agents against noma: Mtk-QSBER model for simultaneous prediction of antibacterial activities and ADMET properties. Mini Rev. Med. Chem., 2015, 15(3), 194-202.
[http://dx.doi.org/10.2174/138955751503150312120519] [PMID: 25769968]
[114]
Speck-Planche, A.; Cordeiro, M.N.D.S. Computer-aided discovery in antimicrobial research: In silico model for virtual screening of potent and safe anti-pseudomonas agents. Comb. Chem. High Throughput Screen., 2015, 18(3), 305-314.
[http://dx.doi.org/10.2174/1386207318666150305144249] [PMID: 25747443]
[115]
Speck-Planche, A.; Cordeiro, M.N.D.S. Chemoinformatics for medicinal chemistry: In silico model to enable the discovery of potent and safer anti-cocci agents. Future Med. Chem., 2014, 6(18), 2013-2028.
[http://dx.doi.org/10.4155/fmc.14.136] [PMID: 25531966]
[116]
Speck-Planche, A.; Cordeiro, M.N.D.S. Review of current chemoinformatic tools for modeling important aspects of CYPs mediated drug metabolism. Integrating metabolism data with other biological profiles to enhance drug discovery. Curr. Drug Metab., 2014, 15(4), 429-440.
[http://dx.doi.org/10.2174/1389200215666140605124002] [PMID: 24909424]
[117]
Speck-Planche, A.; Cordeiro, M.N.D.S. Simultaneous virtual prediction of anti-Escherichia coli activities and ADMET profiles: A chemoinformatic complementary approach for high-throughput screening. ACS Comb. Sci., 2014, 16(2), 78-84.
[http://dx.doi.org/10.1021/co400115s] [PMID: 24383958]
[118]
Speck-Planche, A.; Cordeiro, M.N.D.S. Simultaneous modeling of antimycobacterial activities and ADMET profiles: A chemoinformatic approach to medicinal chemistry. Curr. Top. Med. Chem., 2013, 13(14), 1656-1665.
[http://dx.doi.org/10.2174/15680266113139990116] [PMID: 23889052]
[119]
González-Díaz, H.; Arrasate, S.; Gómez-SanJuan, A.; Sotomayor, N.; Lete, E.; Besada-Porto, L.; Ruso, J.M. General theory for multiple input-output perturbations in complex molecular systems. 1. Linear QSPR electronegativity models in physical, organic, and medicinal chemistry. Curr. Top. Med. Chem., 2013, 13(14), 1713-1741.
[http://dx.doi.org/10.2174/1568026611313140011] [PMID: 23889050]
[120]
Speck-Planche, A.; Cordeiro, M.N.D.S. Advanced in silico approaches for drug discovery: Mining information from multiple biological and chemical data through mtk- QSBER and pt-QSPR Strategies. Curr. Med. Chem., 2017, 24(16), 1687-1704.
[http://dx.doi.org/10.2174/0929867324666170124152746] [PMID: 28120706]
[121]
Simón-Vidal, L.; García-Calvo, O.; Oteo, U.; Arrasate, S.; Lete, E.; Sotomayor, N.; González-Díaz, H. Perturbation-theory and machine learning (ptml) model for high-throughput screening of parham reactions: Experimental and theoretical studies. J. Chem. Inf. Model., 2018, 58(7), 1384-1396.
[http://dx.doi.org/10.1021/acs.jcim.8b00286] [PMID: 29898360]
[122]
Ferreira da Costa, J.; Silva, D.; Caamaño, O.; Brea, J.M.; Loza, M.I.; Munteanu, C.R.; Pazos, A.; García-Mera, X.; González-Díaz, H. Perturbation theory/machine learning model of ChEMBL Data for dopamine targets: Docking, synthesis, and assay of new l-Prolyl-l-leucyl-glycinamide peptidomimetics. ACS Chem. Neurosci., 2018, 9(11), 2572-2587.
[http://dx.doi.org/10.1021/acschemneuro.8b00083] [PMID: 29791132]
[123]
Bediaga, H.; Arrasate, S.; González-Díaz, H. PTML combinatorial model of ChEMBL compounds assays for multiple types of cancer. ACS Comb. Sci., 2018, 20(11), 621-632.
[http://dx.doi.org/10.1021/acscombsci.8b00090] [PMID: 30240186]
[124]
Martínez-Arzate, S.G.; Tenorio-Borroto, E.; Barbabosa Pliego, A.; Díaz-Albiter, H.M.; Vázquez-Chagoyán, J.C.; González-Díaz, H. PTML model for proteome mining of B-Cell epitopes and theoretical-experimental study of bm86 protein sequences from colima, mexico. J. Proteome Res., 2017, 16(11), 4093-4103.
[http://dx.doi.org/10.1021/acs.jproteome.7b00477] [PMID: 28922600]
[125]
Herrera-Ibatá, D.M.; Pazos, A.; Orbegozo-Medina, R.A.; Romero-Durán, F.J.; González-Díaz, H. Mapping chemical structure activity information of HAART-drug cocktails over complex networks of AIDS epidemiology and socioeconomic data of U.S. counties. Biosystems, 2015, 132-133, 20-34.
[http://dx.doi.org/10.1016/j.biosystems.2015.04.007] [PMID: 25916548]
[126]
Herrera-Ibata, D.M.; Orbegozo-Medina, R.A.; Gonzalez-Diaz, H. Multiscale mapping of AIDS in U.S. countries vs anti-HIV drugs activity with complex networks and information indices. Curr. Bioinform., 2015, 10, 639-657.
[http://dx.doi.org/10.2174/1574893610666151008012648]
[127]
Abeijon, P.; Garcia-Mera, X.; Caamano, O.; Yanez, M.; Lopez-Castro, E.; Romero-Duran, F.J.; Gonzalez-Diaz, H. Multi-Target mining of alzheimer disease proteome with Hansch’s QSBR Perturbation theory and experimental-theoretic study of new thiophene isosters of rasagiline. Curr. Drug Targets, 2017, 18(5), 511-521.
[http://dx.doi.org/10.2174/1389450116666151102095243] [PMID: 26521774]
[128]
González-Díaz, H.; Pérez-Montoto, L.G.; Ubeira, F.M. Model for vaccine design by prediction of B-epitopes of IEDB given perturbations in peptide sequence, in vivo process, experimental techniques, and source or host organisms. J. Immunol. Res., 2014., 2014768515.
[http://dx.doi.org/10.1155/2014/768515] [PMID: 24741624]
[129]
González-Díaz, H.; Herrera-Ibatá, D.M.; Duardo-Sánchez, A.; Munteanu, C.R.; Orbegozo-Medina, R.A.; Pazos, A. ANN multiscale model of anti-HIV drugs activity vs AIDS prevalence in the US at county level based on information indices of molecular graphs and social networks. J. Chem. Inf. Model., 2014, 54(3), 744-755.
[http://dx.doi.org/10.1021/ci400716y] [PMID: 24521170]
[130]
Gonzalez-Diaz, H.; Arrasate, S.; Juan, A.G.; Sotomayor, N.; Lete, E.; Speck-Planche, A.; Ruso, J.M.; Luan, F.; Cordeiro, M.N.D.S. Matrix trace operators: From spectral moments of molecular graphs and complex networks to perturbations in synthetic reactions, micelle nanoparticles, and drug ADME processes. Curr. Drug Metab., 2014, 15(4), 470-488.
[http://dx.doi.org/10.2174/1389200215666140908101604] [PMID: 25204825]
[131]
Speck-Planche, A. Recent advances in fragment-based computational drug design: Tackling simultaneous targets/biological effects. Future Med. Chem., 2018, 10(17), 2021-2024.
[http://dx.doi.org/10.4155/fmc-2018-0213] [PMID: 30058835]
[132]
Mesalam, A.A.; Vercauteren, K.; Meuleman, P. Mouse systems to model hepatitis C virus treatment and associated resistance. Viruses, 2016, 8(6), 176.
[http://dx.doi.org/10.3390/v8060176] [PMID: 27338446]
[133]
Ryan, K.J.; Ray, C.G. Sherris Medical Microbiology. An Introduction to infectious diseases, 4th ed; McGraw-Hill Companies, Inc: Arizona, 2004.
[134]
Kaye, K.S.; Pogue, J.M. Infections caused by resistant Gram negative bacteria: Epidemiology and management. Pharmacotherapy, 2015, 35(10), 949-962.
[http://dx.doi.org/10.1002/phar.1636] [PMID: 26497481]
[135]
Lima, V.D.; Gill, V.S.; Yip, B.; Hogg, R.S.; Montaner, J.S.; Harrigan, P.R. Increased resilience to the development of drug resistance with modern boosted protease inhibitor-based highly active antiretroviral therapy. J. Infect. Dis., 2008, 198(1), 51-58.
[http://dx.doi.org/10.1086/588675] [PMID: 18498238]
[136]
Negro, F. Adverse effects of drugs in the treatment of viral hepatitis. Best Pract. Res. Clin. Gastroenterol., 2010, 24(2), 183-192.
[http://dx.doi.org/10.1016/j.bpg.2009.10.012] [PMID: 20227031]
[137]
Croes, S.; Koop, A.H.; van Gils, S.A.; Neef, C. Efficacy, nephrotoxicity and ototoxicity of aminoglycosides, mathematically modelled for modelling-supported therapeutic drug monitoring. Eur. J. Pharm. Sci., 2012, 45(1-2), 90-100.
[http://dx.doi.org/10.1016/j.ejps.2011.10.022] [PMID: 22094306]
[138]
Torres, R.A.; Lewis, W. Aging and HIV/AIDS: Pathogenetic role of therapeutic side effects. Lab. Invest., 2014, 94(2), 120-128.
[http://dx.doi.org/10.1038/labinvest.2013.142] [PMID: 24336070]
[139]
Luther, J.; Glesby, M.J. Dermatologic adverse effects of antiretroviral therapy: Recognition and management. Am. J. Clin. Dermatol., 2007, 8(4), 221-233.
[http://dx.doi.org/10.2165/00128071-200708040-00004] [PMID: 17645377]
[140]
Cortés-Ciriano, I.; Firth, N.C.; Bender, A.; Watson, O. Discovering highly potent molecules from an initial set of inactives using iterative screening. J. Chem. Inf. Model., 2018, 58(9), 2000-2014.
[http://dx.doi.org/10.1021/acs.jcim.8b00376] [PMID: 30130102]
[141]
Mohd Hanafiah, K.; Groeger, J.; Flaxman, A.D.; Wiersma, S.T. Global epidemiology of hepatitis C virus infection: New estimates of age-specific antibody to HCV seroprevalence. Hepatology, 2013, 57(4), 1333-1342.
[http://dx.doi.org/10.1002/hep.26141] [PMID: 23172780]
[142]
Speck-Planche, A.; Dias Soeiro Cordeiro, M.N. Speeding up early drug discovery in antiviral research: A fragment-based in silico approach for the design of virtual anti-hepatitis C leads. ACS Comb. Sci., 2017, 19(8), 501-512.
[http://dx.doi.org/10.1021/acscombsci.7b00039] [PMID: 28437091]
[143]
Lautenbach, E.; Abrutyn, E. Healthcare-Acquired bacterial infections. Bacterial Infections of Humans: Epidemiology and Control, Springer Science+Business Media: LLC: New York, . 2009, 543-575.
[http://dx.doi.org/10.1007/978-0-387-09843-2_26]
[144]
Jenkins, D.R. Nosocomial infections and infection control. Medicine (Baltimore), 2017, 45, 629-633.
[http://dx.doi.org/10.1016/j.mpmed.2017.07.005]
[145]
Speck-Planche, A.; Cordeiro, M.N.D.S. De novo computational design of compounds virtually displaying potent antibacterial activity and desirable in vitro ADMET profiles. Med. Chem. Res., 2017, 26, 2345-2356.
[http://dx.doi.org/10.1007/s00044-017-1936-4]
[146]
Maartens, G.; Celum, C.; Lewin, S.R. HIV infection: Epidemiology, pathogenesis, treatment, and prevention. Lancet, 2014, 384(9939), 258-271.
[http://dx.doi.org/10.1016/S0140-6736(14)60164-1] [PMID: 24907868]
[147]
Granich, R.; Gupta, S.; Hersh, B.; Williams, B.; Montaner, J.; Young, B.; Zuniga, J.M. Trends in AIDS deaths, new infections and ART coverage in the top 30 countries with the highest AIDS mortality burden; 1990-2013. PLoS One, 2015, 10(7), e0131353.
[http://dx.doi.org/10.1371/journal.pone.0131353] [PMID: 26147987]
[148]
Kleandrova, V.V.; Speck Planche, A. Multitasking model for computer-aided design and virtual screening of compounds with high anti-HIV activity and desirable ADMET properties. Multi-Scale Approaches in Drug Discovery: From Empirical Knowledge to In Silico Experiments and Back, 1; Elsevier: Oxford, UK, 2017, pp. 55-81.
[http://dx.doi.org/10.1016/B978-0-08-101129-4.00003-5]

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