Experimental and Computational Approaches to Improve Binding Affinity in Chemical Biology and Drug Discovery

Anuraj       Nayarisseri
doi:10.2174/156802662019200701164759
Abstract

Drug discovery is one of the most complicated processes and establishment of a single drug may require multidisciplinary attempts to design efficient and commercially viable drugs. The main purpose of drug design is to identify a chemical compound or inhibitor that can bind to an active site of a specific cavity on a target protein. The traditional drug design methods involved various experimental based approaches including random screening of chemicals found in nature or can be synthesized directly in chemical laboratories. Except for the long cycle design and time, high cost is also the major issue of concern. Modernized computer-based algorithm including structure-based drug design has accelerated the drug design and discovery process adequately. Surprisingly from the past decade remarkable progress has been made concerned with all area of drug design and discovery. CADD (Computer Aided Drug Designing) based tools shorten the conventional cycle size and also generate chemically more stable and worthy compounds and hence reduce the drug discovery cost. This special edition of editorial comprises the combination of seven research and review articles set emphasis especially on the computational approaches along with the experimental approaches using a chemical synthesizing for the binding affinity in chemical biology and discovery as a salient used in de-novo drug designing. This set of articles exfoliates the role that systems biology and the evaluation of ligand affinity in drug design and discovery for the future.
Keywords: CADD (Computer Aided Drug Designing), Molecular Docking, Virtual screening, ADMET, QSAR, Drug Discovery.
[1] 
Cohen, M.L. Changing patterns of infectious disease. Nature,  2000, 406(6797), 762-767.
[http://dx.doi.org/10.1038/35021206] [PMID:  10963605] 
[2] 
Scannell, J.W.; Blanckley, A.; Boldon, H.; Warrington, B. Diagnosing the decline in pharmaceutical R&D efficiency. Nat. Rev. Drug Discov.,  2012, 11(3), 191-200.
[http://dx.doi.org/10.1038/nrd3681] [PMID:  22378269] 
[3] 
Breman, J. G.; Alilio, M. S.; Mills, A. Conquering the intolerable burden of malaria: what’s new, what’s needed: a summary. Am. J. Trop. Med. Hyg.,  2004, 71(2), 1-15.
[http://dx.doi.org/10.4269/ajtmh.2004.71.2_suppl.0700001] 
[4] 
Zimmet, P.Z.; Magliano, D.J.; Herman, W.H.; Shaw, J.E. Diabetes: a 21st century challenge. Lancet Diabetes Endocrinol.,  2014, 2(1), 56-64.
[http://dx.doi.org/10.1016/S2213-8587(13)70112-8] [PMID:  24622669] 
[5] 
Drews, J. Drug discovery: a historical perspective. Science,  2000, 287(5460), 1960-1964.
[http://dx.doi.org/10.1126/science.287.5460.1960] [PMID:  10720314] 
[6] 
Hughes, J.P.; Rees, S.; Kalindjian, S.B.; Philpott, K.L. Principles of early drug discovery. Br. J. Pharmacol.,  2011, 162(6), 1239-1249.
[http://dx.doi.org/10.1111/j.1476-5381.2010.01127.x] [PMID:  21091654] 
[7] 
Madhukar, N.S.; Khade, P.K.; Huang, L.; Gayvert, K.; Galletti, G.; Stogniew, M.; Allen, J.E.; Giannakakou, P.; Elemento, O. A Bayesian machine learning approach for drug target identification using diverse data types. Nat. Commun.,  2019, 10(1), 5221.
[http://dx.doi.org/10.1038/s41467-019-12928-6] [PMID:  31745082] 
[8] 
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] 
[9] 
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] 
[10] 
Irwin, J.J.; Sterling, T.; Mysinger, M.M.; Bolstad, E.S.; Coleman, R.G. ZINC: a free tool to discover chemistry for biology. J. Chem. Inf. Model.,  2012, 52(7), 1757-1768.
[http://dx.doi.org/10.1021/ci3001277] [PMID:  22587354] 
[11] 
McGuire, R.; Verhoeven, S.; Vass, M.; Vriend, G.; de Esch, I.J.; Lusher, S.J.; Leurs, R.; Ridder, L.; Kooistra, A.J.; Ritschel, T.; de Graaf, C. 3D-e-Chem-VM: structural cheminformatics research infrastructure in a freely available virtual machine. J. Chem. Inf. Model.,  2017, 57(2), 115-121.
[http://dx.doi.org/10.1021/acs.jcim.6b00686] [PMID:  28125221] 
[12] 
Vogt, H.; Hofmann, B.; Getz, L. The new holism: P4 systems medicine and the medicalization of health and life itself. Med. Health Care Philos.,  2016, 19(2), 307-323.
[http://dx.doi.org/10.1007/s11019-016-9683-8] [PMID:  26821201] 
[13] 
Pan, W.H.; Lynn, K.S.; Chen, C.H.; Wu, Y.L.; Lin, C.Y.; Chang, H.Y. Using endophenotypes for pathway clusters to map complex disease genes. Genet. Epidemiol.,  2006, 30(2), 143-154.
[http://dx.doi.org/10.1002/gepi.20136] [PMID:  16437587] 
[14] 
Chen, J.; Xu, H.; Aronow, B.J.; Jegga, A.G. Improved human disease candidate gene prioritization using mouse phenotype. BMC Bioinformatics,  2007, 8(1), 392.
[http://dx.doi.org/10.1186/1471-2105-8-392] [PMID:  17939863] 
[15] 
Nanba, R.; Tada, M.; Kuroda, S.; Houkin, K.; Iwasaki, Y. Sequence analysis and bioinformatics analysis of chromosome 17q25 in familial moyamoya disease. Childs Nerv. Syst.,  2005, 21(1), 62-68.
[http://dx.doi.org/10.1007/s00381-004-1005-4] [PMID:  15340753] 
[16] 
Marshall, G.R. Computer-aided drug design. Annu. Rev. Pharmacol. Toxicol.,  1987, 27(1), 193-213.
[http://dx.doi.org/10.1146/annurev.pa.27.040187.001205] [PMID:  3555315] 
[17] 
Schlicker, A.; Lengauer, T.; Albrecht, M. Improving disease gene prioritization using the semantic similarity of Gene Ontology terms. Bioinformatics,  2010, 26(18), i561-i567.
[http://dx.doi.org/10.1093/bioinformatics/btq384] [PMID:  20823322] 
[18] 
Ramos, R.G.; Olden, K. Gene-environment interactions in the development of complex disease phenotypes. Int. J. Environ. Res. Public Health,  2008, 5(1), 4-11.
[http://dx.doi.org/10.3390/ijerph5010004] [PMID:  18441400] 
[19] 
Gaulton, K.J.; Mohlke, K.L.; Vision, T.J. A computational system to select candidate genes for complex human traits. Bioinformatics,  2007, 23(9), 1132-1140.
[http://dx.doi.org/10.1093/bioinformatics/btm001] [PMID:  17237041] 
[20] 
Lee, S.; Kim, J.Y.; Hwang, J.; Kim, S.; Lee, J.H.; Han, D.H. Investigation of pathogenic genes in peri-implantitis from implant clustering failure patients: a whole-exome sequencing pilot study. PLoS One,  2014, 9(6)e99360
[http://dx.doi.org/10.1371/journal.pone.0099360] [PMID:  24921256] 
[21] 
Rossi, E.; Rossi, K.; Yount, G.; Cozzolino, M.; Iannotti, S. The bioinformatics of integrative medical insights: Proposals for an international psycho-social and cultural bioinformatics project. Integr. Med. Insights,  2006, 1117863370600100002
[http://dx.doi.org/10.1177/117863370600100002] 
[22] 
Mardinoglu, A.; Agren, R.; Kampf, C.; Asplund, A.; Uhlen, M.; Nielsen, J. Genome-scale metabolic modelling of hepatocytes reveals serine deficiency in patients with non-alcoholic fatty liver disease. Nat. Commun.,  2014, 5(1), 3083.
[http://dx.doi.org/10.1038/ncomms4083] [PMID:  24419221] 
[23] 
del Sol, A.; Balling, R.; Hood, L.; Galas, D. Diseases as network perturbations. Curr. Opin. Biotechnol.,  2010, 21(4), 566-571.
[http://dx.doi.org/10.1016/j.copbio.2010.07.010] [PMID:  20709523] 
[24] 
Limaye, A.; Sweta, J.; Madhavi, M.; Mudgal, U.; Mukherjee, S.; Sharma, S.; Hussain, T.; Nayarisseri, A.; Singh, S.K. In silico insights on gd2: a potential target for pediatric neuroblastoma. Curr. Top. Med. Chem.,  2019, 19(30), 2766-2781.
[http://dx.doi.org/10.2174/1568026619666191112115333] [PMID:  31721713] 
[25] 
Douguet, D.; Munier-Lehmann, H.; Labesse, G.; Pochet, S. LEA3D: a computer-aided ligand design for structure-based drug design. J. Med. Chem.,  2005, 48(7), 2457-2468.
[http://dx.doi.org/10.1021/jm0492296] [PMID:  15801836] 
[26] 
Nayarisseri, A. Prospects of utilizing computational techniques for the treatment of human diseases. Curr. Top. Med. Chem.,  2019, 19(13), 1071-1074.
[http://dx.doi.org/10.2174/156802661913190827102426] [PMID:  31490742] 
[27] 
Khan, T.; Ahmad, R.; Azad, I.; Raza, S.; Joshi, S.; Khan, A.R. Computer-aided drug design and virtual screening of targeted combinatorial libraries of mixed-ligand transition metal complexes of 2-butanone thiosemicarbazone. Comput. Biol. Chem.,  2018, 75, 178-195.
[http://dx.doi.org/10.1016/j.compbiolchem.2018.05.008] [PMID:  29883916] 
[28] 
Nasr, A.B.; Ponnala, D.; Sagurthi, S.R.; Kattamuri, R.K.; Marri, V.K.; Gudala, S.; Lakkaraju, C.; Bandaru, S.; Nayarisseri, A. Molecular Docking studies of FKBP12-mTOR inhibitors using binding predictions. Bioinformation,  2015, 11(6), 307-315.
[http://dx.doi.org/10.6026/97320630011307] [PMID:  26229292] 
[29] 
Dunna, N.R.; Kandula, V.; Girdhar, A.; Pudutha, A.; Hussain, T.; Bandaru, S.; Nayarisseri, A. High affinity pharmacological profiling of dual inhibitors targeting RET and VEGFR2 in inhibition of kinase and angiogeneis events in medullary thyroid carcinoma. Asian Pac. J. Cancer Prev.,  2015, 16(16), 7089-7095.
[http://dx.doi.org/10.7314/APJCP.2015.16.16.7089] [PMID:  26514495] 
[30] 
Suzuki, E.; Akutsu, T.; Ohsuga, S. Knowledge-based system for computer-aided drug design. Knowl. Base. Syst.,  1993, 6(2), 114-126.
[http://dx.doi.org/10.1016/0950-7051(93)90026-P] 
[31] 
Tapon, F.; Thong, M.; Bartell, M. Drug discovery and development in four Canadian biotech companies. R & D Manag.,  2001, 31(1), 77-90.
[http://dx.doi.org/10.1111/1467-9310.00198] 
[32] 
Geenhuizen, M.V. Knowledge networks of young innovators in the urban economy: biotechnology as a case study. Entrep. Reg. Dev.,  2008, 20(2), 161-183.
[http://dx.doi.org/10.1080/08985620701748318] 
[33] 
Nelson, R.R. Co–evolution of industry structure, technology and supporting institutions, and the making of comparative advantage. Int. J. Econ. Bus.,  1995, 2(2), 171-184.
[http://dx.doi.org/10.1080/758519306] 
[34] 
Blumenthal, D. Growing pains for new academic/industry relationships. Health Aff. (Millwood),  1994, 13(3), 176-193.
[http://dx.doi.org/10.1377/hlthaff.13.3.176] [PMID:  7927148] 
[35] 
Chatterjee, C.; Srinivasan, V. Ethical issues in health care sector in India. IIMB Manag. Rev.,  2013, 25(1), 49-62.
[http://dx.doi.org/10.1016/j.iimb.2012.11.004] 
[36] 
Bowonder, B.; Richardson, P.K. Liberalization and the growth of business led R&D: the case of India. R & D Manag.,  2000, 30(4), 279-288.
[http://dx.doi.org/10.1111/1467-9310.00181] 
[37] 
Juma, C.; Fang, K.; Honca, D.; Huete-Perez, J.; Konde, V.; Lee, S.H.; Singh, S. Global governance of technology: meeting the needs of developing countries. Int. J. Technol. Manag.,  2001, 22(7-8), 629-655.
[http://dx.doi.org/10.1504/IJTM.2001.002982] 
[38] 
Dasgupta, N.; Ranjan, S.; Mundekkad, D.; Ramalingam, C.; Shanker, R.; Kumar, A. Nanotechnology in agro-food: from field to plate. Food Res. Int.,  2015, 69, 381-400.
[http://dx.doi.org/10.1016/j.foodres.2015.01.005] 
[39] 
Akbarsha, M.A.; Zeeshan, M.; Meenekumari, K.J. 
                	alternatives to animals in education, research and risk assessment: an overview with special reference to indian context altex proc
                 , 2013; 2, pp. 5-19.
[40] 
Kaitin, K.I. Deconstructing the drug development process: the new face of innovation. Clin. Pharmacol. Ther.,  2010, 87(3), 356-361.
[http://dx.doi.org/10.1038/clpt.2009.293] [PMID:  20130565] 
[41] 
Bawa, R.; Bawa, S.R.; Maebius, S.B.; Flynn, T.; Wei, C. Protecting new ideas and inventions in nanomedicine with patents. Nanomedicine,  2005, 1(2), 150-158.
[http://dx.doi.org/10.1016/j.nano.2005.03.009] [PMID:  17292072] 
[42] 
Lee, M.S.; Kerns, E.H. LC/MS applications in drug development. Mass Spectrom. Rev.,  1999, 18(3-4), 187-279.
[http://dx.doi.org/10.1002/(SICI)1098-2787(1999)18:3/4<187:AID-MAS2>3.0.CO;2-K] [PMID:  10568041] 
[43] 
Francis, D.; Bessant, J. Targeting innovation and implications for capability development. Technovation,  2005, 25(3), 171-183.
[http://dx.doi.org/10.1016/j.technovation.2004.03.004] 
[44] 
Leckie, G.J.; Pettigrew, K.E.; Sylvain, C. Modeling the information seeking of professionals: A general model derived from research on engineers, health care professionals, and lawyers. Libr. Q.,  1996, 66(2), 161-193.
[http://dx.doi.org/10.1086/602864] 
[45] 
Wright, M.; Clarysse, B.; Lockett, A.; Knockaert, M. Mid-range universities’ linkages with industry: Knowledge types and the role of intermediaries. Res. Policy,  2008, 37(8), 1205-1223.
[http://dx.doi.org/10.1016/j.respol.2008.04.021] 
[46] 
David, B.; Wolfender, J.L.; Dias, D.A. The pharmaceutical industry and natural products: historical status and new trends. Phytochem. Rev.,  2015, 14(2), 299-315.
[http://dx.doi.org/10.1007/s11101-014-9367-z] 
[47] 
Bandaru, S.; Ponnala, D.; Lakkaraju, C.; Bhukya, C.K.; Shaheen, U.; Nayarisseri, A. Identification of high affinity non-peptidic small molecule inhibitors of MDM2-p53 interactions through structure-based virtual screening strategies. Asian Pac. J. Cancer Prev.,  2015, 16(9), 3759-3765.
[http://dx.doi.org/10.7314/APJCP.2015.16.9.3759] [PMID:  25987034] 
[48] 
Ali, M.A.; Vuree, S.; Goud, H.; Hussain, T.; Nayarisseri, A.; Singh, S.K. Identification of high-affinity small molecules targeting gamma secretase for the treatment of alzheimer’s disease. Curr. Top. Med. Chem.,  2019, 19(13), 1173-1187.
[http://dx.doi.org/10.2174/1568026619666190617155326] [PMID:  31244427] 
[49] 
Loew, G.H.; Villar, H.O.; Alkorta, I. Strategies for indirect computer-aided drug design. Pharm. Res.,  1993, 10(4), 475-486.
[http://dx.doi.org/10.1023/A:1018977414572] [PMID:  8483829] 
[50] 
Shaheen, U.; Akka, J.; Hinore, J.S.; Girdhar, A.; Bandaru, S.; Sumithnath, T.G.; Nayarisseri, A.; Munshi, A. Computer aided identification of sodium channel blockers in the clinical treatment of epilepsy using molecular docking tools. Bioinformation,  2015, 11(3), 131-137.
[http://dx.doi.org/10.6026/97320630011131] [PMID:  25914447] 
[51] 
Gudala, S.; Khan, U.; Kanungo, N.; Bandaru, S.; Hussain, T.; Parihar, M.; Nayarisseri, A.; Mundluru, H.P. Identification and pharmacological analysis of high efficacy small molecule inhibitors of EGF-EGFR interactions in clinical treatment of non-small cell lung carcinoma: A computational approach. Asian Pac. J. Cancer Prev.,  2015, 16(18), 8191-8196.
[http://dx.doi.org/10.7314/APJCP.2015.16.18.8191] [PMID:  26745059] 
[52] 
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] 
[53] 
Natchimuthu, V.; Bandaru, S.; Nayarisseri, A.; Ravi, S. Design, synthesis and computational evaluation of a novel intermediate salt of N-cyclohexyl-N-(cyclohexylcarbamoyl)-4-(trifluoromethyl) benzamide as potential potassium channel blocker in epileptic paroxysmal seizures. Comput. Biol. Chem.,  2016, 64, 64-73.
[http://dx.doi.org/10.1016/j.compbiolchem.2016.05.003] [PMID:  27266485] 
[54] 
Sahila, M.M.; Babitha, P.P.; Bandaru, S.; Nayarisseri, A.; Doss, V.A. Molecular docking based screening of GABA (A) receptor inhibitors from plant derivatives. Bioinformation,  2015, 11(6), 280-289.
[http://dx.doi.org/10.6026/97320630011280] [PMID:  26229288] 
[55] 
Kapetanovic, I.M. Computer-aided drug discovery and development (CADDD): in silico-chemico-biological approach. Chem. Biol. Interact.,  2008, 171(2), 165-176.
[http://dx.doi.org/10.1016/j.cbi.2006.12.006] [PMID:  17229415] 
[56] 
Khandekar, N.; Singh, S.; Shukla, R.; Tirumalaraju, S.; Bandaru, S.; Banerjee, T.; Nayarisseri, A. Structural basis for the in vitro known acyl-depsipeptide 2 (ADEP2) inhibition to Clp 2 protease from Mycobacterium tuberculosis. Bioinformation,  2016, 12(3), 92-97.
[http://dx.doi.org/10.6026/97320630012092] [PMID:  28149041] 
[57] 
Zhong, S.; Chen, X.; Zhu, X.; Dziegielewska, B.; Bachman, K.E.; Ellenberger, T.; Ballin, J.D.; Wilson, G.M.; Tomkinson, A.E.; MacKerell, A.D. Jr Identification and validation of human DNA ligase inhibitors using computer-aided drug design. J. Med. Chem.,  2008, 51(15), 4553-4562.
[http://dx.doi.org/10.1021/jm8001668] [PMID:  18630893] 
[58] 
Sharda, S.; Sarmandal, P.; Cherukommu, S.; Dindhoria, K.; Yadav, M.; Bandaru, S.; Sharma, A.; Sakhi, A.; Vyas, T.; Hussain, T.; Nayarisseri, A.; Singh, S.K. A virtual screening approach for the identification of high affinity small molecules targeting bcr-abl1 inhibitors for the treatment of chronic myeloid leukemia. Curr. Top. Med. Chem.,  2017, 17(26), 2989-2996.
[http://dx.doi.org/10.2174/1568026617666170821124512] [PMID:  28828991] 
[59] 
Jain, D.; Udhwani, T.; Sharma, S.; Gandhe, A.; Reddy, P.B.; Nayarisseri, A.; Singh, S.K. Design of novel jak3 inhibitors towards rheumatoid arthritis using molecular docking analysis. Bioinformation,  2019, 15(2), 68-78.
[http://dx.doi.org/10.6026/97320630015068] [PMID:  31435152] 
[60] 
Mendonça-Junior, F.J.B.; Scotti, M.T.; Nayarisseri, A.; Zondegoumba, E.N.T.; Scotti, L. Natural bioactive products with antioxidant properties useful in neurodegenerative diseases. Oxid. Med. Cell. Longev.,  2019, 20197151780
[http://dx.doi.org/10.1155/2019/7151780] [PMID:  31210847] 
[61] 
Nayarisseri, A.; Hood, E.A. Advancement in microbial cheminformatics. Curr. Top. Med. Chem.,  2018, 18(29), 2459-2461.
[http://dx.doi.org/10.2174/1568026619666181120121528] [PMID:  30457050] 
[62] 
Gokhale, P.; Chauhan, A.P.S.; Arora, A.; Khandekar, N.; Nayarisseri, A.; Singh, S.K. FLT3 inhibitor design using molecular docking based virtual screening for acute myeloid leukemia. Bioinformation,  2019, 15(2), 104-115.
[http://dx.doi.org/10.6026/97320630015104] [PMID:  31435156] 
[63] 
Shukla, P.; Khandelwal, R.; Sharma, D.; Dhar, A.; Nayarisseri, A.; Singh, S.K. Virtual screening of il-6 inhibitors for idiopathic arthritis. Bioinformation,  2019, 15(2), 121-130.
[http://dx.doi.org/10.6026/97320630015121] [PMID:  31435158] 
[64] 
Udhwani, T.; Mukherjee, S.; Sharma, K.; Sweta, J.; Khandekar, N.; Nayarisseri, A.; Singh, S.K. Design of PD-L1 inhibitors for lung cancer. Bioinformation,  2019, 15(2), 139-150.
[http://dx.doi.org/10.6026/97320630015139] [PMID:  31435160] 
[65] 
Kelotra, S.; Jain, M.; Kelotra, A.; Jain, I.; Bandaru, S.; Nayarisseri, A.; Bidwai, A. An in silico appraisal to identify high affinity anti-apoptotic synthetic tetrapeptide inhibitors targeting the mammalian caspase 3 enzyme. Asian Pac. J. Cancer Prev.,  2014, 15(23), 10137-10142.
[http://dx.doi.org/10.7314/APJCP.2014.15.23.10137] [PMID:  25556438] 
[66] 
Sweta, J.; Khandelwal, R.; Srinitha, S.; Pancholi, R.; Adhikary, R.; Ali, M.A.; Nayarisseri, A.; Vuree, S.; Singh, S.K. Identification of high-affinity small molecule targeting idh2 for the clinical treatment of acute myeloid leukemia. Asian Pac. J. Cancer Prev.,  2019, 20(8), 2287-2297.
[http://dx.doi.org/10.31557/APJCP.2019.20.8.2287] [PMID:  31450897] 
[67] 
Gutlapalli, V.R.; Sykam, A.; Nayarisseri, A.; Suneetha, S.; Suneetha, L.M. Insights from the predicted epitope similarity between Mycobacterium tuberculosis virulent factors and its human homologs. Bioinformation,  2015, 11(12), 517-524.
[http://dx.doi.org/10.6026/97320630011517] [PMID:  26770024] 
[68] 
Nayarisseri, A.; Yadav, M.; Wishard, R. Computational evaluation of new homologous down regulators of Translationally Controlled Tumor Protein (TCTP) targeted for tumor reversion. Interdiscip. Sci.,  2013, 5(4), 274-279.
[http://dx.doi.org/10.1007/s12539-013-0183-8] [PMID:  24402820] 
[69] 
Praseetha, S.; Bandaru, S.; Nayarisseri, A.; Sureshkumar, S. Pharmacological analysis of vorinostat analogues as potential anti-tumor agents targeting human histone deacetylases: an epigenetic treatment stratagem for cancers. Asian Pac. J. Cancer Prev.,  2016, 17(3), 1571-1576.
[http://dx.doi.org/10.7314/APJCP.2016.17.3.1571] [PMID:  27039807] 
[70] 
Hage-Melim, L.I.D.S.; da Silva, C.H.T.D.P.; Semighini, E.P.; Taft, C.A.; Sampaio, S.V. Computer-aided drug design of novel PLA2 inhibitor candidates for treatment of snakebite. J. Biomol. Struct. Dyn.,  2009, 27(1), 27-36.
[http://dx.doi.org/10.1080/07391102.2009.10507293] [PMID:  19492860] 
[71] 
Sharma, K.; Patidar, K.; Ali, M.A.; Patil, P.; Goud, H.; Hussain, T.; Nayarisseri, A.; Singh, S.K. Structure-based virtual screening for the identification of high affinity compounds as potent vegfr2 inhibitors for the treatment of renal cell carcinoma. Curr. Top. Med. Chem.,  2018, 18(25), 2174-2185.
[http://dx.doi.org/10.2174/1568026619666181130142237] [PMID:  30499413] 
[72] 
Basak, S.C.; Nayarisseri, A.; González-Díaz, H.; Bonchev, D. Editorial (Thematic Issue: chemoinformatics models for pharmaceutical design, Part 2). Curr. Pharm. Des.,  2016, 22(34), 5177-5178.
[http://dx.doi.org/10.2174/138161282234161110222751] [PMID:  27852211] 
[73] 
Basak, S.C.; Nayarisseri, A.; González-Díaz, H.; Bonchev, D. Editorial (Thematic Issue: Chemoinformatics models for pharmaceutical design, Part 1). Curr. Pharm. Des.,  2016, 22(33), 5041-5042.
[http://dx.doi.org/10.2174/138161282233161109224932] [PMID:  27852204] 
[74] 
Kelotra, A.; Gokhale, S.M.; Kelotra, S.; Mukadam, V.; Nagwanshi, K.; Bandaru, S.; Nayarisseri, A.; Bidwai, A. Alkyloxy carbonyl modified hexapeptides as a high affinity compounds for Wnt5A protein in the treatment of psoriasis. Bioinformation,  2014, 10(12), 743-749.
[http://dx.doi.org/10.6026/97320630010743] [PMID:  25670877] 
[75] 
Chandrakar, B.; Jain, A.; Roy, S.; Gutlapalli, V.R.; Saraf, S.; Suppahia, A.; Verma, A.; Tiwari, A.; Yadav, M.; Nayarisseri, A. Molecular modeling of Acetyl-CoA carboxylase (ACC) from Jatrophacurcas and virtual screening for identification of inhibitors. J. Pharm. Res.,  2013, 6(9), 913-918.
[76] 
Zeng, H.; Wu, X. Alzheimer’s disease drug development based on Computer-Aided Drug Design. Eur. J. Med. Chem.,  2016, 121, 851-863.
[http://dx.doi.org/10.1016/j.ejmech.2015.08.039] [PMID:  26415837] 
[77] 
Nayarisseri, A.; Singh, S.K. Functional inhibition of vegf and egfr suppressors in cancer treatment. Curr. Top. Med. Chem.,  2019, 19(3), 178-179.
[http://dx.doi.org/10.2174/156802661903190328155731] [PMID:  30950335] 
[78] 
Monteiro, A.F.M.; Viana, J.O.; Nayarisseri, A.; Zondegoumba, E.N.; Mendoncajunior,;a, F.J.B.junior, Junior; Scotti, M.T.; Scotti, L. Computational studies applied to flavonoids against alzheimer’s and parkinson’s diseases. Oxid. Med. Cell. Longev.,  2018, 20187912765
[http://dx.doi.org/10.1155/2018/7912765] [PMID:  30693065] 
[79] 
Patidar, K.; Panwar, U.; Vuree, S.; Sweta, J.; Sandhu, M.K.; Nayarisseri, A.; Singh, S.K. An in silico approach to identify high affinity small molecule targeting m-tor inhibitors for the clinical treatment of breast cancer. Asian Pac. J. Cancer Prev.,  2019, 20(4), 1229-1241.
[http://dx.doi.org/10.31557/APJCP.2019.20.4.1229] [PMID:  31030499] 
[80] 
Sharda, S.; Khandelwal, R.; Adhikary, R.; Sharma, D.; Majhi, M.; Hussain, T. A computer-aided drug designing for pharmacological inhibition of ALK inhibitors induces apoptosis and differentiation in Non-small cell lung cancer. Curr. Top. Med. Chem.,  2019, 19(13), 1129-1144.
[http://dx.doi.org/10.2174/1568026619666190521084941] [PMID:  31109278] 
[81] 
Aher, A.; Udhwani, T.; Khandelwal, R.; Limaye, A.; Hussain, T.; Nayarisseri, A.; Singh, S.K. Aher, A.; Udhwani, T.; Khandelwal, R.; Limaye, A.; Hussain, T.; Nayarisseri, A.; Singh, S.K. in silico insights on il-6: a potential target for multicentric castleman disease. Curr Comput Aided Drug Des, 2019, 15 (ePub ahead of Print)
[http://dx.doi.org/10.2174/1573409915666190902142524] [PMID:   31475901] 
[82] 
Adhikary, R.; Khandelwal, R.; Hussain, T.; Nayarisseri, A.; Singh, S.K.  Adhikary, R.; Khandelwal, R.; Hussain, T.; Nayarisseri, A.; Singh, S.K. Structural insights into the molecular design of ros1 inhibitor for the treatment of non-small cell lung cancer (nsclc). Curr Comput Aided Drug Des, 2020. (ePub ahead of Print)
[http://dx.doi.org/10.2174/1573409916666200504105249] [PMID:  32364080] 
[83] 
Ekins, S.; Nikolsky, Y.; Nikolskaya, T. Techniques: application of systems biology to absorption, distribution, metabolism, excretion and toxicity. Trends Pharmacol. Sci.,  2005, 26(4), 202-209.
[http://dx.doi.org/10.1016/j.tips.2005.02.006] [PMID:  15808345] 
[84] 
Kell, D.B. Systems biology, metabolic modelling and metabolomics in drug discovery and development. Drug Discov. Today,  2006, 11(23-24), 1085-1092.
[http://dx.doi.org/10.1016/j.drudis.2006.10.004] [PMID:  17129827] 
[85] 
Antman, E.; Weiss, S.; Loscalzo, J. Systems pharmacology, pharmacogenetics, and clinical trial design in network medicine. Wiley Interdiscip. Rev. Syst. Biol. Med.,  2012, 4(4), 367-383.
[http://dx.doi.org/10.1002/wsbm.1173] [PMID:  22581565] 
[86] 
Trombetta, E.S.; Mellman, I. Cell biology of antigen processing in vitro and in vivo. Annu. Rev. Immunol.,  2005, 23, 975-1028.
[http://dx.doi.org/10.1146/annurev.immunol.22.012703.104538] [PMID:  15771591] 
[87] 
Shelby, M.D.; Newbold, R.R.; Tully, D.B.; Chae, K.; Davis, V.L. Assessing environmental chemicals for estrogenicity using a combination of in vitro and in vivo assays. Environ. Health Perspect.,  1996, 104(12), 1296-1300.
[http://dx.doi.org/10.1289/ehp.961041296] [PMID:  9118870] 
[88] 
Ziats, N.P.; Miller, K.M.; Anderson, J.M. In vitro and in vivo interactions of cells with biomaterials. Biomaterials,  1988, 9(1), 5-13.
[http://dx.doi.org/10.1016/0142-9612(88)90063-4] [PMID:  3280039] 
[89] 
Albanese, A.; Tang, P.S.; Chan, W.C. The effect of nanoparticle size, shape, and surface chemistry on biological systems. Annu. Rev. Biomed. Eng.,  2012, 14, 1-16.
[http://dx.doi.org/10.1146/annurev-bioeng-071811-150124] [PMID:  22524388] 
[90] 
Poulsen, L.K. In vivo and in vitro techniques to determine the biological activity of food allergens. J. Chromatogr. B Biomed. Sci. Appl.,  2001, 756(1-2), 41-55.
[http://dx.doi.org/10.1016/S0378-4347(01)00070-6] [PMID:  11419727] 
[91] 
Movilla, N.; Bustelo, X.R. Biological and regulatory properties of Vav-3, a new member of the Vav family of oncoproteins. Mol. Cell. Biol.,  1999, 19(11), 7870-7885.
[http://dx.doi.org/10.1128/MCB.19.11.7870] [PMID:  10523675] 
[92] 
Boucaut, J.C.; Darribère, T.; Poole, T.J.; Aoyama, H.; Yamada, K.M.; Thiery, J.P. Biologically active synthetic peptides as probes of embryonic development: a competitive peptide inhibitor of fibronectin function inhibits gastrulation in amphibian embryos and neural crest cell migration in avian embryos. J. Cell Biol.,  1984, 99(5), 1822-1830.
[http://dx.doi.org/10.1083/jcb.99.5.1822] [PMID:  6490722] 
[93] 
Edgington, T. S.; Mackman, N.; Brand, K.; Ruf, W. Edgington, T. S.; Mackman, N.; Brand, K.; Ruf, W. The structural biology of expression and function of tissue factor Thrombosis and haemostasis,  1991, 65(01), 067-079.
[http://dx.doi.org/dx.doi.org/10.1055/s-0038-1646375] 
[94] 
Yazici, H.; Fong, H.; Wilson, B.; Oren, E.E.; Amos, F.A.; Zhang, H.; Evans, J.S.; Snead, M.L.; Sarikaya, M.; Tamerler, C. Biological response on a titanium implant-grade surface functionalized with modular peptides. Acta Biomater.,  2013, 9(2), 5341-5352.
[http://dx.doi.org/10.1016/j.actbio.2012.11.004] [PMID:  23159566] 
[95] 
Parnas, O.; Jovanovic, M.; Eisenhaure, T.M.; Herbst, R.H.; Dixit, A.; Ye, C.J.; Przybylski, D.; Platt, R.J.; Tirosh, I.; Sanjana, N.E.; Shalem, O.; Satija, R.; Raychowdhury, R.; Mertins, P.; Carr, S.A.; Zhang, F.; Hacohen, N.; Regev, A. A genome-wide CRISPR screen in primary immune cells to dissect regulatory networks. Cell,  2015, 162(3), 675-686.
[http://dx.doi.org/10.1016/j.cell.2015.06.059] [PMID:  26189680] 
[96] 
Carpenter, A.E.; Sabatini, D.M. Systematic genome-wide screens of gene function. Nat. Rev. Genet.,  2004, 5(1), 11-22.
[http://dx.doi.org/10.1038/nrg1248] [PMID:  14708012] 
[97] 
Chen, S.; Sanjana, N.E.; Zheng, K.; Shalem, O.; Lee, K.; Shi, X.; Scott, D.A.; Song, J.; Pan, J.Q.; Weissleder, R.; Lee, H.; Zhang, F.; Sharp, P.A. Genome-wide CRISPR screen in a mouse model of tumor growth and metastasis. Cell,  2015, 160(6), 1246-1260.
[http://dx.doi.org/10.1016/j.cell.2015.02.038] [PMID:  25748654] 
[98] 
Barrangou, R.; Doudna, J.A. Applications of CRISPR technologies in research and beyond. Nat. Biotechnol.,  2016, 34(9), 933-941.
[http://dx.doi.org/10.1038/nbt.3659] [PMID:  27606440] 
[99] 
Fei, T.; Chen, Y.; Xiao, T.; Li, W.; Cato, L.; Zhang, P.; Cotter, M.B.; Bowden, M.; Lis, R.T.; Zhao, S.G.; Wu, Q.; Feng, F.Y.; Loda, M.; He, H.H.; Liu, X.S.; Brown, M. Genome-wide CRISPR screen identifies HNRNPL as a prostate cancer dependency regulating RNA splicing. Proc. Natl. Acad. Sci. USA,  2017, 114(26), E5207-E5215.
[http://dx.doi.org/10.1073/pnas.1617467114] [PMID:  28611215] 
[100] 
Xue, H.Y.; Ji, L.J.; Gao, A.M.; Liu, P.; He, J.D.; Lu, X.J. CRISPR-Cas9 for medical genetic screens: applications and future perspectives. J. Med. Genet.,  2016, 53(2), 91-97.
[http://dx.doi.org/10.1136/jmedgenet-2015-103409] [PMID:  26673779] 
[101] 
Toledo, C.M.; Ding, Y.; Hoellerbauer, P.; Davis, R.J.; Basom, R.; Girard, E.J.; Lee, E.; Corrin, P.; Hart, T.; Bolouri, H.; Davison, J.; Zhang, Q.; Hardcastle, J.; Aronow, B.J.; Plaisier, C.L.; Baliga, N.S.; Moffat, J.; Lin, Q.; Li, X.N.; Nam, D.H.; Lee, J.; Pollard, S.M.; Zhu, J.; Delrow, J.J.; Clurman, B.E.; Olson, J.M.; Paddison, P.J. Genome-wide CRISPR-Cas9 screens reveal loss of redundancy between PKMYT1 and WEE1 in glioblastoma stem-like cells. Cell Rep.,  2015, 13(11), 2425-2439.
[http://dx.doi.org/10.1016/j.celrep.2015.11.021] [PMID:  26673326] 
[102] 
Xiang, M.; Cao, Y.; Fan, W.; Chen, L.; Mo, Y. Computer-aided drug design: lead discovery and optimization. Comb. Chem. High Throughput Screen.,  2012, 15(4), 328-337.
[http://dx.doi.org/10.2174/138620712799361825] [PMID:  22221065] 
[103] 
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] 
[104] 
Warshel, A.; Tao, H.; Fothergill, M.; Chu, Z.T. Effective methods for estimation of binding energies in computer‐aided drug design. Isr. J. Chem.,  1994, 34(2), 253-256.
[http://dx.doi.org/10.1002/ijch.199400029] 
[105] 
Bharatam, P.V.; Khanna, S. m; Francis, S.M. Bharatam, P. V.; Khanna, S.; Francis, S. M. Modeling and informatics in drug design. In: Preclinical Development Handbook: ADME and Biopharmaceutical Properties; Wiley online library: Hoboken, , 2010; pp. 1-46.
[http://dx.doi.org/10.1002/9780470571224.pse031] 
[106] 
Carr, R.A.; Congreve, M.; Murray, C.W.; Rees, D.C. Fragment-based lead discovery: leads by design. Drug Discov. Today,  2005, 10(14), 987-992.
[http://dx.doi.org/10.1016/S1359-6446(05)03511-7] [PMID:  16023057] 
[107] 
Macarron, R.; Banks, M.N.; Bojanic, D.; Burns, D.J.; Cirovic, D.A.; Garyantes, T.; Green, D.V.; Hertzberg, R.P.; Janzen, W.P.; Paslay, J.W.; Schopfer, U.; Sittampalam, G.S. Impact of high-throughput screening in biomedical research. Nat. Rev. Drug Discov.,  2011, 10(3), 188-195.
[http://dx.doi.org/10.1038/nrd3368] [PMID:  21358738] 
[108] 
Keserű, 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] 
[109] 
Macarrón, R.; Hertzberg, R.P. Design and implementation of high throughput screening assays. Mol. Biotechnol.,  2011, 47(3), 270-285.
[http://dx.doi.org/10.1007/s12033-010-9335-9] [PMID:  20865348] 
[110] 
Mayr, L.M.; Bojanic, D. Novel trends in high-throughput screening. Curr. Opin. Pharmacol.,  2009, 9(5), 580-588.
[http://dx.doi.org/10.1016/j.coph.2009.08.004] [PMID:  19775937] 
[111] 
Mishra, K.P.; Ganju, L.; Sairam, M.; Banerjee, P.K.; Sawhney, R.C. A review of high throughput technology for the screening of natural products. Biomed. Pharmacother.,  2008, 62(2), 94-98.
[http://dx.doi.org/10.1016/j.biopha.2007.06.012] [PMID:  17692498] 
[112] 
Mayr, L.M.; Fuerst, P. The future of high-throughput screening. J. Biomol. Screen.,  2008, 13(6), 443-448.
[http://dx.doi.org/10.1177/1087057108319644] [PMID:  18660458] 
[113] 
Kumar, V.; Krishna, S.; Siddiqi, M.I. Virtual screening strategies: recent advances in the identification and design of anti-cancer agents. Methods,  2015, 71, 64-70.
[http://dx.doi.org/10.1016/j.ymeth.2014.08.010] [PMID:  25171960] 
[114] 
Glick, M.; Jacoby, E. The role of computational methods in the identification of bioactive compounds. Curr. Opin. Chem. Biol.,  2011, 15(4), 540-546.
[http://dx.doi.org/10.1016/j.cbpa.2011.02.021] [PMID:  21411361] 
[115] 
Bottegoni, G.; Favia, A.D.; Recanatini, M.; Cavalli, A. The role of fragment-based and computational methods in polypharmacology. Drug Discov. Today,  2012, 17(1-2), 23-34.
[http://dx.doi.org/10.1016/j.drudis.2011.08.002] [PMID:  21864710] 
[116] 
Polgár, T.; Keseru, G.M. Integration of virtual and high throughput screening in lead discovery settings. Comb. Chem. High Throughput Screen.,  2011, 14(10), 889-897.
[http://dx.doi.org/10.2174/138620711797537148] [PMID:  21843143] 
[117] 
Muegge, I.; Enyedy, I.J. Virtual screening for kinase targets. Curr. Med. Chem.,  2004, 11(6), 693-707.
[http://dx.doi.org/10.2174/0929867043455684] [PMID:  15032724] 
[118] 
Grant, M.A. Protein structure prediction in structure-based ligand design and virtual screening. Comb. Chem. High Throughput Screen.,  2009, 12(10), 940-960.
[http://dx.doi.org/10.2174/138620709789824718] [PMID:  20025561] 
[119] 
Kim, K.H.; Kim, N.D.; Seong, B.L. Pharmacophore-based virtual screening: a review of recent applications. Expert Opin. Drug Discov.,  2010, 5(3), 205-222.
[http://dx.doi.org/10.1517/17460441003592072] [PMID:  22823018] 
[120] 
Evers, A.; Hessler, G.; Matter, H.; Klabunde, T. Virtual screening of biogenic amine-binding G-protein coupled receptors: comparative evaluation of protein- and ligand-based virtual screening protocols. J. Med. Chem.,  2005, 48(17), 5448-5465.
[http://dx.doi.org/10.1021/jm050090o] [PMID:  16107144] 
[121] 
da Silva, C.H.; da Silva, V.B.; Resende, J.; Rodrigues, P.F.; Bononi, F.C.; Benevenuto, C.G.; Taft, C.A. Computer-aided drug design and ADMET predictions for identification and evaluation of novel potential farnesyltransferase inhibitors in cancer therapy. J. Mol. Graph. Model.,  2010, 28(6), 513-523.
[http://dx.doi.org/10.1016/j.jmgm.2009.11.011] [PMID:  20074987] 
[122] 
Foloppe, N.; Fisher, L.M.; Howes, R.; Potter, A.; Robertson, A.G.; Surgenor, A.E. Identification of chemically diverse Chk1 inhibitors by receptor-based virtual screening. Bioorg. Med. Chem.,  2006, 14(14), 4792-4802.
[http://dx.doi.org/10.1016/j.bmc.2006.03.021] [PMID:  16574416] 
[123] 
Oprea, T.I.; Matter, H. Integrating virtual screening in lead discovery. Curr. Opin. Chem. Biol.,  2004, 8(4), 349-358.
[http://dx.doi.org/10.1016/j.cbpa.2004.06.008] [PMID:  15288243] 
[124] 
Cheng, H.; Linhares, B.M.; Yu, W.; Cardenas, M.G.; Ai, Y.; Jiang, W.; Winkler, A.; Cohen, S.; Melnick, A.; MacKerell, A., Jr; Cierpicki, T.; Xue, F. Identification of thiourea-based inhibitors of the B-cell lymphoma 6 BTB domain via NMR-based fragment screening and computer-aided drug design. J. Med. Chem.,  2018, 61(17), 7573-7588.
[http://dx.doi.org/10.1021/acs.jmedchem.8b00040] [PMID:  29969259] 
[125] 
Durrant, J.D.; McCammon, J.A. Potential drug-like inhibitors of Group 1 influenza neuraminidase identified through computer-aided drug design. Comput. Biol. Chem.,  2010, 34(2), 97-105.
[http://dx.doi.org/10.1016/j.compbiolchem.2010.03.005] [PMID:  20427241] 
[126] 
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] 
[127] 
Ahmadi, M.; Nowroozi, A.; Shahlaei, M. Constructing an atomic-resolution model of human P2X7 receptor followed by pharmacophore modeling to identify potential inhibitors. J. Mol. Graph. Model.,  2015, 61, 243-261.
[http://dx.doi.org/10.1016/j.jmgm.2015.08.005] [PMID:  26298810] 
[128] 
Kaserer, T.; Beck, K.R.; Akram, M.; Odermatt, A.; Schuster, D. Pharmacophore models and pharmacophore-based virtual screening: concepts and applications exemplified on hydroxysteroid dehydrogenases. Molecules,  2015, 20(12), 22799-22832.
[http://dx.doi.org/10.3390/molecules201219880] [PMID:  26703541] 
[129] 
Llorach-Pares, L.; Nonell-Canals, A.; Sanchez-Martinez, M.; Avila, C. Computer-aided drug design applied to marine drug discovery: Meridianins as Alzheimer’s disease therapeutic agents. Mar. Drugs,  2017, 15(12), 366.
[http://dx.doi.org/10.3390/md15120366] [PMID:  29186912] 
[130] 
Lavecchia, A.; Cerchia, C. In silico methods to address polypharmacology: current status, applications and future perspectives. Drug Discov. Today,  2016, 21(2), 288-298.
[http://dx.doi.org/10.1016/j.drudis.2015.12.007] [PMID:  26743596] 
[131] 
Gao, Q.; Yang, L.; Zhu, Y. Pharmacophore based drug design approach as a practical process in drug discovery. Curr Comput Aided Drug Des,  2010, 6(1), 37-49.
[http://dx.doi.org/10.2174/157340910790980151] [PMID:  20370694] 
[132] 
Kortagere, S.; Lill, M.; Kerrigan, J. Kortagere, S.; Lill, M.; Kerrigan, J. Role of computational methods in pharmaceutical sciences. In: Computational Toxicology; Humana Press: Totowa, NJ, Role of computational methods in pharmaceutical sciences in.In: Computational Toxicology; Humana Press: Totowa, NJ, 2012; pp. 21-48.
[http://dx.doi.org/10.1007/978-1-62703-050-2_3] 
[133] 
Ekins, S.; Freundlich, J.S.; Choi, I.; Sarker, M.; Talcott, C. Computational databases, pathway and cheminformatics tools for tuberculosis drug discovery. Trends Microbiol.,  2011, 19(2), 65-74.
[http://dx.doi.org/10.1016/j.tim.2010.10.005] [PMID:  21129975] 
[134] 
Heikamp, K.; Bajorath, J. Support vector machines for drug discovery. Expert Opin. Drug Discov.,  2014, 9(1), 93-104.
[http://dx.doi.org/10.1517/17460441.2014.866943] [PMID:  24304044] 
[135] 
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.
[http://dx.doi.org/10.1186/s40169-017-0181-2] [PMID:  29340951] 
[136] 
Thai, N.Q.; Nguyen, H.L.; Linh, H.Q.; Li, M.S. Protocol for fast screening of multi-target drug candidates: application to alzheimer’s disease. J. Mol. Graph. Model.,  2017, 77, 121-129.
[http://dx.doi.org/10.1016/j.jmgm.2017.08.002] [PMID:  28850894] 
[137] 
De Simone, A.; La Pietra, V.; Betari, N.; Petragnani, N.; Conte, M.; Daniele, S.; Pietrobono, D.; Martini, C.; Petralla, S.; Casadei, R.; Davani, L.; Frabetti, F.; Russomanno, P.; Novellino, E.; Montanari, S.; Tumiatti, V.; Ballerini, P.; Sarno, F.; Nebbioso, A.; Altucci, L.; Monti, B.; Andrisano, V.; Milelli, A. Discovery of the first-in-class gsk-3β/hdac dual inhibitor as disease-modifying agent to combat alzheimer’s disease. ACS Med. Chem. Lett.,  2019, 10(4), 469-474.
[http://dx.doi.org/10.1021/acsmedchemlett.8b00507] [PMID:  30996781] 
[138] 
Cummins, P.L.; Gready, J.E. Computer-aided drug design: a free energy perturbation study on the binding of methyl-substituted pterins and N5-deazapterins to dihydrofolate reductase. J. Comput. Aided Mol. Des.,  1993, 7(5), 535-555.
[http://dx.doi.org/10.1007/BF00124361] [PMID:  8294945] 
[139] 
Proschak, E.; Stark, H.; Merk, D. Polypharmacology by design: a medicinal chemist’s perspective on multitargeting compounds. J. Med. Chem.,  2019, 62(2), 420-444.
[http://dx.doi.org/10.1021/acs.jmedchem.8b00760] [PMID:  30035545] 
[140] 
Miglianico, M.; Nicolaes, G.A.; Neumann, D. Pharmacological targeting of AMP-activated protein kinase and opportunities for computer-aided drug design. Miniperspective. J. Med. Chem.,  2016, 59(7), 2879-2893.
[http://dx.doi.org/10.1021/acs.jmedchem.5b01201] [PMID:  26510622] 
[141] 
Abdolmaleki, A.; Ghasemi, F.; Ghasemi, J.B. Computer-aided drug design to explore cyclodextrin therapeutics and biomedical applications. Chem. Biol. Drug Des.,  2017, 89(2), 257-268.
[http://dx.doi.org/10.1111/cbdd.12825] [PMID:  28205401] 
[142] 
Sydow, D.; Wichmann, M.; Rodríguez-Guerra, J.; Goldmann, D.; Landrum, G.; Volkamer, A. Teachopencadd-knime: a teaching platform for computer-aided drug design using knime workflows. J. Chem. Inf. Model.,  2019, 59(10), 4083-4086.
[http://dx.doi.org/10.1021/acs.jcim.9b00662] [PMID:  31612715] 
[143] 
Sanders, M.P.; Barbosa, A.J.; Zarzycka, B.; Nicolaes, G.A.; Klomp, J.P.; de Vlieg, J.; Del Rio, A. Comparative analysis of pharmacophore screening tools. J. Chem. Inf. Model.,  2012, 52(6), 1607-1620.
[http://dx.doi.org/10.1021/ci2005274] [PMID:  22646988] 
[144] 
Mezey, P.G. Computer aided drug design: Some fundamental aspects. J. Mol. Model.,  2000, 6(2), 150-157.
[145] 
Tang, Y.; Zhu, W.; Chen, K.; Jiang, H. New technologies in computer-aided drug design: Toward target identification and new chemical entity discovery. Drug Discov. Today. Technol.,  2006, 3(3), 307-313.
[http://dx.doi.org/10.1016/j.ddtec.2006.09.004] [PMID:  24980533] 
[146] 
Cerqueira, N.M.; Gesto, D.; Oliveira, E.F.; Santos-Martins, D.; Brás, N.F.; Sousa, S.F.; Fernandes, P.A.; Ramos, M.J. Receptor-based virtual screening protocol for drug discovery. Arch. Biochem. Biophys.,  2015, 582, 56-67.
[http://dx.doi.org/10.1016/j.abb.2015.05.011] [PMID:  26045247] 
[147] 
Clark, D.E. What has virtual screening ever done for drug discovery? Expert Opin. Drug Discov.,  2008, 3(8), 841-851.
[http://dx.doi.org/10.1517/17460441.3.8.841] [PMID:  23484962] 
[148] 
Kambouris, M.E.; Manoussopoulos, Y.; Kantzanou, M.; Velegraki, A.; Gaitanis, G.; Arabatzis, M.; Patrinos, G.P. Rebooting bioresilience: a multi-omics approach to tackle global catastrophic biological risks and next-generation biothreats. OMICS,  2018, 22(1), 35-51.
[http://dx.doi.org/10.1089/omi.2017.0185] [PMID:  29356627] 
[149] 
Prajapati, L.; Khandelwal, R.; Yogalakshmi, K.N.; Munshi, A.; Nayarisseri, A. Computer-aided structure prediction of bluetongue virus coat protein vp2 assisted by optimized potential for liquid simulations(opls). Curr. Top. Med. Chem.,  2020, 20(19), 1716-1728.
[http://dx.doi.org/10.2174/1568026620666200516153753] [PMID:  32416694] 
[150] 
Tripathi, P.N.; Srivastava, P.; Sharma, P.; Tripathi, M.K.; Seth, A.; Tripathi, A.; Rai, S.N.; Singh, S.P.; Shrivastava, S.K. Biphenyl-3-oxo-1,2,4-triazine linked piperazine derivatives as potential cholinesterase inhibitors with anti-oxidant property to improve the learning and memory. Bioorg. Chem.,  2019, 85, 82-96.
[http://dx.doi.org/10.1016/j.bioorg.2018.12.017] [PMID:  30605887] 
[151] 
Ladani, G.G.; Patel, M.P. Novel 1, 3, 4-oxadiazole motifs bearing a quinoline nucleus: synthesis, characterization and biological evaluation of their antimicrobial, antitubercular, antimalarial and cytotoxic activities. New J. Chem.,  2015, 39(12), 9848-9857.
[http://dx.doi.org/10.1039/C5NJ02566D] 
[152] 
Maru, M.S.; Shah, M.K. Synthesis, characterization and biological evaluation of mononuclear dichloro-bis [2-(2-chloro-6, 7-substituted quinolin-3-yl)-1h-benzo [d] imidazole] co (ii) complexes. orbital: the electronic. J. Chem.,  2015, 7(2), 108-121.
[153] 
Rao, K. V. R.; Mani, P.; Satyanarayana, B.; Rao, T. R. purification and structural elucidation of three bioactive compounds isolated from streptomyces	coelicoflavus bc 01 and their biological activity 3 biotech 2017, 7(1), 24.
[154] 
Desai, N.C.; Patel, B.Y.; Dave, B.P. Synthesis and antimicrobial activity of novel quinoline derivatives bearing pyrazoline and pyridine analogues. Med. Chem. Res.,  2017, 26(1), 109-119.
[http://dx.doi.org/10.1007/s00044-016-1732-6] 
[155] 
Youn, K.; Jun, M. In vitro BACE1 inhibitory activity of geraniin and corilagin from Geranium thunbergii. Planta Med.,  2013, 79(12), 1038-1042.
[http://dx.doi.org/10.1055/s-0032-1328769] [PMID:  23877922] 
[156] 
Lin, S.Y.; Wang, C.C.; Lu, Y.L.; Wu, W.C.; Hou, W.C. Antioxidant, anti-semicarbazide-sensitive amine oxidase, and anti-hypertensive activities of geraniin isolated from Phyllanthus urinaria. Food Chem. Toxicol.,  2008, 46(7), 2485-2492.
[http://dx.doi.org/10.1016/j.fct.2008.04.007] [PMID:  18495318] 
[157] 
Notka, F.; Meier, G.; Wagner, R. Concerted inhibitory activities of Phyllanthus amarus on HIV replication in vitro and ex vivo. Antiviral Res.,  2004, 64(2), 93-102.
[http://dx.doi.org/10.1016/S0166-3542(04)00129-9] [PMID:  15498604] 
[158] 
Hidari, K.I.; Abe, T.; Suzuki, T. Carbohydrate-related inhibitors of dengue virus entry. Viruses,  2013, 5(2), 605-618.
[http://dx.doi.org/10.3390/v5020605] [PMID:  23389466] 
[159] 
Ayala-Nuñez, N.V.; Jarupathirun, P.; Kaptein, S.J.; Neyts, J.; Smit, J.M. Antibody-dependent enhancement of dengue virus infection is inhibited by SA-17, a doxorubicin derivative. Antiviral Res.,  2013, 100(1), 238-245.
[http://dx.doi.org/10.1016/j.antiviral.2013.08.013] [PMID:  23994499] 
[160] 
Laurini, E.; Col, V.D.; Mamolo, M.G.; Zampieri, D.; Posocco, P.; Fermeglia, M.; Vio, L.; Pricl, S. Homology model and docking-based virtual screening for ligands of the σ1 receptor. ACS Med. Chem. Lett.,  2011, 2(11), 834-839.
[http://dx.doi.org/10.1021/ml2001505] [PMID:  24900272] 
[161] 
Sarkar, S.; Gupta, S.; Chakraborty, W.; Senapati, S.; Gachhui, R. 
                    Sarkar, S.; Gupta, S.; Chakraborty, W.; Senapati, S.; Gachhui, R. Homology modeling, molecular docking and molecular dynamics studies of the catalytic domain of chitin deacetylase from Cryptococcus laurentii strain RY1. Int. J. Biol. Macromol., 2017, 1682-1691. 104(Pt B),
[http://dx.doi.org/10.1016/j.ijbiomac.2017.03.057] [PMID:  28315437] 
[162] 
Durrant, J.D.; McCammon, J.A. Molecular dynamics simulations and drug discovery. BMC Biol.,  2011, 9(1), 71.
[http://dx.doi.org/10.1186/1741-7007-9-71] [PMID:  22035460] 
[163] 
Singh, R.; Sobhia, M.E. Structure prediction and molecular dynamics simulations of a G-protein coupled receptor: human CCR2 receptor. J. Biomol. Struct. Dyn.,  2013, 31(7), 694-715.
[http://dx.doi.org/10.1080/07391102.2012.707460] [PMID:  22909007] 
[164] 
Marelius, J.; Kolmodin, K.; Feierberg, I.; Åqvist, J.Q. a molecular dynamics program for free energy calculations and empirical valence bond simulations in biomolecular systems. J. Mol. Graph. Model.,  1998, 16(4-6), 213-225, 261.
[http://dx.doi.org/10.1016/S1093-3263(98)80006-5] [PMID:  10522241] 
[165] 
Zhou, H.; Wang, C.; Ye, J.; Chen, H.; Tao, R. Zhou, H.; Wang, C.; Ye, J.; Chen, H.; Tao, R. Design, virtual screening, molecular docking and molecular dynamics studies of novel urushiol derivatives as potential HDAC2 selective inhibitors. Gene, 2017, 637, 63-71.
[http://dx.doi.org/10.1016/j.gene.2017.09.034 ] [PMID:  28939339] 
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DOI https://dx.doi.org/10.2174/156802662019200701164759	Print ISSN 1568-0266
Publisher Name Bentham Science Publisher	Online ISSN 1873-4294
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