Generic placeholder image

Mini-Reviews in Medicinal Chemistry


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

Review Article

Applications of Quantitative Structure-Activity Relationships (QSAR) based Virtual Screening in Drug Design: A Review

Author(s): Patnala Ganga Raju Achary*

Volume 20, Issue 14, 2020

Page: [1375 - 1388] Pages: 14

DOI: 10.2174/1389557520666200429102334

Price: $65


The scientists, and the researchers around the globe generate tremendous amount of information everyday; for instance, so far more than 74 million molecules are registered in Chemical Abstract Services. According to a recent study, at present we have around 1060 molecules, which are classified as new drug-like molecules. The library of such molecules is now considered as ‘dark chemical space’ or ‘dark chemistry.’ Now, in order to explore such hidden molecules scientifically, a good number of live and updated databases (protein, cell, tissues, structure, drugs, etc.) are available today. The synchronization of the three different sciences: ‘genomics’, proteomics and ‘in-silico simulation’ will revolutionize the process of drug discovery. The screening of a sizable number of drugs like molecules is a challenge and it must be treated in an efficient manner. Virtual screening (VS) is an important computational tool in the drug discovery process; however, experimental verification of the drugs also equally important for the drug development process. The quantitative structure-activity relationship (QSAR) analysis is one of the machine learning technique, which is extensively used in VS techniques. QSAR is well-known for its high and fast throughput screening with a satisfactory hit rate. The QSAR model building involves (i) chemo-genomics data collection from a database or literature (ii) Calculation of right descriptors from molecular representation (iii) establishing a relationship (model) between biological activity and the selected descriptors (iv) application of QSAR model to predict the biological property for the molecules. All the hits obtained by the VS technique needs to be experimentally verified. The present mini-review highlights: the web-based machine learning tools, the role of QSAR in VS techniques, successful applications of QSAR based VS leading to the drug discovery and advantages and challenges of QSAR.

Keywords: QSAR, virtual screening, computer-assisted drug design, ligand based screening, reverse pharmacology, genomics.

Graphical Abstract
Mukherjee, P.K.; Harwansh, R.K.; Bahadur, S.; Banerjee, S.; Kar, A.; Chanda, J. Development of ayurveda - Tradition to trend. J. Ethnopharmacol., 2017, 197,
Mannangatti, P.; Naidu, K.N. indian herbs for the treatment of neurodegenerative disease. Adv. Neurobiol., 2016, 12, 323-336.
[] [PMID: 27651261]
Ven Murthy, M.R.; Ranjekar, P.K.; Ramassamy, C.; Deshpande, M. Scientific basis for the use of Indian ayurvedic medicinal plants in the treatment of neurodegenerative disorders: Ashwagandha. Cent. Nerv. Syst. Agents Med. Chem., 2010, 10(3), 238-246.
[] [PMID: 20528765]
Patwardhan, B. Bridging Ayurveda with evidence-based scientific approaches in medicine. EPMA J., 2014, 5(1), 19.
[] [PMID: 25395997]
Singh, R.H. Exploring issues in the development of ayurvedic research methodology. J. Ayurveda Integr. Med., 2010, 1(2), 91-95.
Chauhan, A.; Semwal, D.; Mishra, S.; Semwal, R. Ayurvedic research and methodology: Present status and future strategies. Ayu, 2015, 36(4), 364-369.
[PMID: 27833362]
Pandey, M.M.; Rastogi, S.; Rawat, A.K.S. Indian traditional ayurvedic system of medicine and nutritional supplementation. Evid. Based Complement. Alternat. Med., 2013, 2013, 376327.
[] [PMID: 23864888]
Lee, J.A.; Uhlik, M.T.; Moxham, C.M.; Tomandl, D.; Sall, D.J. Modern phenotypic drug discovery is a viable, neoclassic pharma strategy. J. Med. Chem., 2012, 55(10), 4527-4538.
Takenaka, T. Classical vs reverse pharmacology in drug discovery. BJU Int., 2001, 88(2), 7-10.discussion 49-50..
Wassermann, A.M.; Bajorath, J. BindingDB and ChEMBL: Online compound databases for drug discovery. Expert Opin. Drug Discov., 2011, 6(7), 683-687.
[] [PMID: 22650976]
Reddy, A.S.; Amarnath, H.S.D.; Bapi, R.S.; Sastry, G.M.; Sastry, G.N. Protein ligand interaction database (PLID). Comput. Biol. Chem., 2008, 32(5), 387-390.
[] [PMID: 18514578]
Rose, P.W.; Prlić, A.; Bi, C.; Bluhm, W.F.; Christie, C.H.; Dutta, S.; Green, R.K.; Goodsell, D.S.; Westbrook, J.D.; Woo, J.; Young, J.; Zardecki, C.; Berman, H.M.; Bourne, P.E.; Burley, S.K. The RCSB Protein Data Bank: Views of structural biology for basic and applied research and education. Nucleic Acids Res., 2015, 43(Database issue), D345-D356.
[] [PMID: 25428375]
Roth, B.L.; Lopez, E.; Patel, S.; Kroeze, W.K. the multiplicity of serotonin receptors: Uselessly diverse molecules or an embarrassment of riches? Neuroscience, 2000, 6(4), 252-262.
Law, V.; Knox, C.; Djoumbou, Y.; Jewison, T.; Guo, A.C.; Liu, Y.; Maciejewski, A.; Arndt, D.; Wilson, M.; Neveu, V.; Tang, A.; Gabriel, G.; Ly, C.; Adamjee, S.; Dame, Z.T.; Han, B.; Zhou, Y.; Wishart, D.S. DrugBank 4.0: Shedding new light on drug metabolism. Nucleic Acids Res., 2014, 42, D1091-D1097.
[] [PMID: 24203711]
Okuno, Y; Tamon, A; Yabuuchi, H; Niijima, S; Minowa, Y; Tonomura, K GLIDA: GPCR - Ligand database for chemical genomics drug discovery - Database and tools update. Nucleic Acids Res., 2008, 361.
Caspi, R.; Altman, T.; Billington, R.; Dreher, K.; Foerster, H.; Fulcher, C.A.; Holland, T.A.; Keseler, I.M.; Kothari, A.; Kubo, A.; Krummenacker, M.; Latendresse, M.; Mueller, L.A.; Ong, Q.; Paley, S.; Subhraveti, P.; Weaver, D.S.; Weerasinghe, D.; Zhang, P.; Karp, P.D. The MetaCyc database of metabolic pathways and enzymes and the BioCyc collection of Pathway/Genome Databases. Nucleic Acids Res., 2014, 42(Database issue), D459-D471.
[] [PMID: 24225315]
Günther, S; Kuhn, M; Dunkel, M; Campillos, M; Senger, C; Petsalaki, E SuperTarget and Matador: Resources for exploring drug-target relationships. Nucleic Acids Res., 2008, 36(Database issue), D919-22.
Davis, A.P.; Grondin, C.J.; Lennon-Hopkins, K.; Saraceni-Richards, C.; Sciaky, D.; King, B.L.; Wiegers, T.C.; Mattingly, C.J. The Comparative Toxicogenomics Database’s 10th year anniversary: Update 2015. Nucleic Acids Res., 2015, 43(Database issue), D914-D920.
[] [PMID: 25326323]
Schaefer, CF; Anthony, K; Krupa, S; Buchoff, J; Day, M; Hannay, T PID: The pathway interaction database. Nucleic Acids Res., 2009, 3, 71.
Croft, D.; Mundo, A.F.; Haw, R.; Milacic, M.; Weiser, J.; Wu, G.; Caudy, M.; Garapati, P.; Gillespie, M.; Kamdar, M.R.; Jassal, B.; Jupe, S.; Matthews, L.; May, B.; Palatnik, S.; Rothfels, K.; Shamovsky, V.; Song, H.; Williams, M.; Birney, E.; Hermjakob, H.; Stein, L.; D’Eustachio, P. The Reactome pathway knowledgebase. Nucleic Acids Res., 2014, 42(Database issue), D472-D477.
[] [PMID: 24243840]
Wang, Y.; Xiao, J.; Suzek, T.O.; Zhang, J.; Wang, J.; Bryant, S.H. PubChem: A public information system for analyzing bioactivities of small molecules. Nucleic Acids Res., 2009, 37, W623-33.
Roy, A.; Skolnick, J. LIGSIFT: An open-source tool for ligand structural alignment and virtual screening. Bioinformatics, 2015, 31(4), 539-544.
[] [PMID: 25336501]
Kitchen, D.B.; Decornez, H.; Furr, J.R.; Bajorath, J. Docking and scoring in virtual screening for drug discovery: Methods and applications. Nat. Rev. Drug Discov., 2004, 3(11), 935-949.
[] [PMID: 15520816]
McInnes, C. Virtual screening strategies in drug discovery. Curr. Opin. Chem. Biol., 2007, 11(5), 494-502.
[] [PMID: 17936059]
Loging, W.; Harland, L.; Williams-Jones, B. High-throughput electronic biology: Mining information for drug discovery; Nature Reviews Drug Discovery.Nature Publishing Group; , 2007, Vol. 6, pp. 220-230.
Walker, T.; Grulke, C.M.; Pozefsky, D.; Tropsha, A. Chembench: A cheminformatics workbench. Bioinformatics, 2010, 26(23), 3000-3001.
[] [PMID: 20889496]
Sakakibara, Y.; Hachiya, T.; Uchida, M.; Nagamine, N.; Sugawara, Y.; Yokota, M. COPICAT: A software system for predicting interactions between proteins and chemical compounds., 2012.
Liu, X.; Vogt, I.; Haque, T.; Campillos, M. HitPick: A web server for hit identification and target prediction of chemical screenings. Bioinformatics, 2013, 29(15), 1910-1912.
[] [PMID: 23716196]
Liu, B.; Wei, Y.; Zhang, Y.; Yang, Q. Deep neural networks for high dimension, low sample size data. IJCAI International Joint Conference on Artificial Intelligence, 2017, 2287-2293.
Kuhn, M.; Szklarczyk, D.; Pletscher-Frankild, S.; Blicher, T.H.; von Mering, C.; Jensen, L.J. STITCH 4: Integration of protein–chemical interactions with user data. Nucleic Acids Res., 2014, 42(D1), D401-7.
Luo, H.; Zhang, P.; Cao, X.H.; Du, D.; Ye, H.; Huang, H.; Li, C.; Qin, S.; Wan, C.; Shi, L.; He, L.; Yang, L. DPDR-CPI, a server that predicts drug positioning and drug repositioning via chemical protein interactome. Sci. Rep., 2016, 6, 35996.
[] [PMID: 27805045]
Labbé, C.M.; Pencheva, T.; Jereva, D.; Desvillechabrol, D.; Becot, J.; Villoutreix, B.O.; Pajeva, I.; Miteva, M.A. AMMOS2: A web server for protein-ligand-water complexes refinement via molecular mechanics. Nucleic Acids Res., 2017, 45(W1), W350-W355.
[] [PMID: 28486703]
Wang, J.C.; Chu, P.Y.; Chen, C.M.; Lin, J.H. idTarget: A web server for identifying protein targets of small chemical molecules with robust scoring functions and a divide-and-conquer docking approach. Nucleic Acids Res., 201240(Web Server issue). , W393-W399.
[] [PMID: 22649057]
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., 200634(Web Server issue); , W219-W24. [WEB. SERV. ISS.].
[] [PMID: 16844997]
Grosdidier, A; Zoete, V; Michielin, O. SwissDock, a protein-small molecule docking web service based on EADock DSS. Nucleic Acids Res., 201139(Web Server issue). , W270-7.
Xie, X.Q.; Chen, J.Z. Data mining a small molecule drug screening representative subset from NIH PubChem. J. Chem. Inf. Model., 2008, 48(3), 465-475.
[] [PMID: 18302356]
Kuhn, M; von Mering, C; Campillos, M; Jensen, LJ; Bork, P STITCH: Interaction networks of chemicals and proteins. Nucleic Acids Res., 2008, 36, 1.
Sushko, I.; Novotarskyi, S.; Körner, R.; Pandey, A.K.; Rupp, M.; Teetz, W.; Brandmaier, S.; Abdelaziz, A.; Prokopenko, V.V.; Tanchuk, V.Y.; Todeschini, R.; Varnek, A.; Marcou, G.; Ertl, P.; Potemkin, V.; Grishina, M.; Gasteiger, J.; Schwab, C.; Baskin, I.I.; Palyulin, V.A.; Radchenko, E.V.; Welsh, W.J.; Kholodovych, V.; Chekmarev, D.; Cherkasov, A.; Aires-de-Sousa, J.; Zhang, Q.Y.; Bender, A.; Nigsch, F.; Patiny, L.; Williams, A.; Tkachenko, V.; Tetko, I.V. Online chemical modeling environment (OCHEM): Web platform for data storage, model development and publishing of chemical information. J. Comput. Aided Mol. Des., 2011, 25(6), 533-554.
[] [PMID: 21660515]
Capuzzi, S.J.; Kim, I.S-J.; Lam, W.I.; Thornton, T.E.; Muratov, E.N.; Pozefsky, D. Chembench: A publicly accessible, integrated cheminformatics portal. J. Chem. Inf. Model., 2017, 57(2), 105-8.
Labbé, C.M.; Rey, J.; Lagorce, D.; Vavruša, M.; Becot, J.; Sperandio, O.; Villoutreix, B.O.; Tufféry, P.; Miteva, M.A. MTiOpenScreen: A web server for structure-based virtual screening. Nucleic Acids Res., 2015, 43(W1), W448-W454.
[] [PMID: 25855812]
Morris, G.M.; Huey, R.; Lindstrom, W.; Sanner, M.F.; Belew, R.K.; Goodsell, D.S.; Olson, A.J. AutoDock4 and AutoDock-Tools4: Automated docking with selective receptor flexibility. J. Comput. Chem., 2009, 30(16), 2785-2791.
[] [PMID: 19399780]
Trott, O.; Olson, A.J. AutoDock Vina: Improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading. J. Comput. Chem., 2010, 31(2), 455-461.
[PMID: 19499576]
Wang, Y.; Suzek, T.; Zhang, J.; Wang, J.; He, S.; Cheng, T. 2013, PubChem BioAssay: 2014 update Nucleic Acids Res., 2013, 42(Database issue), D1075-D1082.
Zhang, Z.; Martiny, V.; Lagorce, D.; Ikeguchi, Y.; Alexov, E.; Miteva, M.A. Rational design of small-molecule stabilizers of spermine synthase dimer by virtual screening and free energy-based approach. PLoS One, 2014, 9(10), e110884.
[] [PMID: 25340632]
Arkin, M.R.; Wells, J.A. Small-molecule inhibitors of protein protein interactions: Progressing towards the dream. Nat. Rev. Drug Discov., 2004, 3(4), 301-317.
[] [PMID: 15060526]
Mullard, A. Protein-protein interaction inhibitors get into the groove. Nat. Rev. Drug Discov., 2012, 11(3), 173-175.
[] [PMID: 22378255]
Szklarczyk, D.; Santos, A.; von Mering, C.; Jensen, L.J.; Bork, P.; Kuhn, M. STITCH 5: Augmenting protein–chemical interaction networks with tissue and affinity data. Nucleic Acids Res., 2016, 44(D1), D380-D384.
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.
[] [PMID: 21948594]
Soufan, O.; Ba-Alawi, W.; Magana-Mora, A.; Essack, M.; Bajic, V.B. DPubChem: A web tool for QSAR modeling and high throughput virtual screening. Sci. Rep., 2018, 8(1), 9110.
[] [PMID: 29904147]
Caine, M.; Raz, S.; Zeigler, M. Adrenergic and cholinergic receptors in the human prostate, prostatic capsule and bladder neck Br. J. Urol., 1975, 47(2), 193-202.
Takenaka, T.; Honda, K.; Fujikura, T.; Niigata, K.; Tachikawa, S.; Inukai, N. New sulfamoylphenethylamines, potent α1-adrenoceptor antagonists. J. Pharm. Pharmacol., 1984, 36(8),
O’Reilly, L.P.; Long, O.S.; Cobanoglu, M.C.; Benson, J.A.; Luke, C.J.; Miedel, M.T.; Hale, P.; Perlmutter, D.H.; Bahar, I.; Silverman, G.A.; Pak, S.C. A genome-wide RNAi screen identifies potential drug targets in a C. elegans model of α1-antitrypsin deficiency. Hum. Mol. Genet., 2014, 23(19), 5123-5132.
[] [PMID: 24838285]
Mattmann, C.A. A vision for data science. Nature, 2013, 493(7433), 473-5.
Lombardino, J.G.; Lowe, J.A. The role of the medicinal chemist in drug discovery — then and now. Nat. Rev. Drug Discov., 2004, 3(10),
Long, M.; Schonfeld, R. Supporting the changing research practices of chemists., 2015.
Medina-Franco, J.L.; Giulianotti, M.A.; Welmaker, G.S.; Houghten, R.A. Shifting from the single to the multitarget paradigm in drug discovery. Drug Discov. Today, 2013, 18(9-10), 495-501.
[] [PMID: 23340113]
Acharya, C.; Coop, A. Recent advances in ligand-based drug design: Relevance and utility of the conformationally sampled pharmacophore approach. Curr. Comput. Aided-Drug Des., 2011, 7(1),
Lusher, S.J.; McGuire, R.; van Schaik, R.C.; Nicholson, C.D.; de Vlieg, J. Data-driven medicinal chemistry in the era of big data. Drug Discov. Today, 2014, 19(7), 859-68.
Schwikowski, B.; Uetz, P.; Fields, S. A network of protein-protein interactions in yeast. Nat. Biotechnol., 2000, 18(12), 1257-1261.
[] [PMID: 11101803]
Kim Kjærulff, S.; Wich, L.; Kringelum, J.; Jacobsen, U.P.; Kouskoumvekaki, I.; Audouze, K. ChemProt-2.0: Visual navigation in a disease chemical biology database. Nucleic Acids Res., 2012, 41(Database issue), D464-D469.
Kanehisa, M.; Goto, S.; Sato, Y.; Kawashima, M.; Furumichi, M.; Tanabe, M. Data, information, knowledge and principle: Back to metabolism in KEGG. Nucleic Acids Res., 2013, 42(Database issue), D199-D205.
Chatr-Aryamontri, A.; Breitkreutz, B-J.; Heinicke, S.; Boucher, L.; Winter, A.; Stark, C. The BioGRID interaction database: 2013 update. Nucleic Acids Res., 2012, 41(Database issue), D816-D823.
Hopkins, A.L.; Groom, C.R.; Alex, A. Ligand efficiency: A useful metric for lead selection. Drug Discov. Today, 2004, 9(10), 430-431.
[] [PMID: 15109945]
Hopkins, A.L. Network pharmacology: The next paradigm in drug discovery. Nat. Chem. Biol., 2008, 4(11), 682-690.
[] [PMID: 18936753]
Lyu, J.; Wang, S.; Balius, T.E.; Singh, I.; Levit, A.; Moroz, Y.S. Ultra-large library docking for discovering new chemotypes. Nature, 2019, 566(7743), 224-229.
Ultra-large virtual molecular libraries throw open chemical space. Nature,
Noble, D. Will genomics revolutionise pharmaceutical RD? Trends Biotechnol., 2003, 21(8), 333-337.
Loew, G.H.; Villar, H.O.; Alkorta, I. Strategies for indirect computer-aided drug design. Pharm. Res., 1993, 10(4),
Mason, J.; Good, A.; Martin, E. 3-D pharmacophores in drug discovery. Curr. Pharm. Des., 2011, 7(7), 567-597.
[PMID: 11375769]
Karelson, M.; Sild, S.; Maran, U. Non-linear QSAR treatment of genotoxicity. Mol. Simul., 2000.
Verma, J.; Khedkar, V.; Coutinho, E. 3D-QSAR in Drug Design - A Review. Curr. Top. Med. Chem., 2010, 10(1),
Abuhammad, A.; Taha, M.O. QSAR studies in the discovery of novel type-II diabetic therapies. Expert Opin. Drug Discov., 2016, 11(2), 197-214.
Peter, S.C.; Dhanjal, J.K.; Malik, V.; Radhakrishnan, N.; Jayakanthan, M.; Sundar, D. Quantitative Structure-Activity Relationship (QSAR): Modeling approaches to biological applications Encyclopedia of Bioinformatics and Computational Biology , 2018.
Schetz, J.A. Structure-Activity Relationships: Theory, Uses and Limitations; Reference Module in Biomedical Sciences, 2016.
Mitchell, J.B.O. Machine learning methods in chemoinformatics Wiley Interdiscip. Rev. Comput. Mol. Sci., 2014, 4(5), 468-481.
Cherkasov, A.; Muratov, E.N.; Fourches, D.; Varnek, A.; Baskin, I.I.; Cronin, M. QSAR modeling: Where have you been? Where are you going to? J. Med. Chem., 2014, 57(12),
Ekins, S.; de Siqueira-Neto, J.L.; McCall, L.I.; Sarker, M.; Yadav, M.; Ponder, E.L.; Kallel, E.A.; Kellar, D.; Chen, S.; Arkin, M.; Bunin, B.A.; McKerrow, J.H.; Talcott, C. Machine learning models and pathway genome data base for trypanosoma cruzi drug discovery. PLoS Negl. Trop. Dis., 2015, 9(6), e0003878.
[] [PMID: 26114876]
Goh, G.B.; Hodas, N.O.; Vishnu, A. Deep learning for computational chemistry. J. Comput. Chem., 2017, 38(16), 1291-1307.
[] [PMID: 28272810]
Williams, A.J.; Ekins, S. A quality alert and call for improved curation of public chemistry databases. Drug Discov. Today, 2011, 16(17-18), 747-50.
Southan, C.; Várkonyi, P.; Muresan, S. Quantitative assessment of the expanding complementarity between public and commercial databases of bioactive compounds. J. Cheminform., 2009, 1(1),
Young, D.; Martin, T.; Venkatapathy, R.; Harten, P. Are the chemical structures in your QSAR correct? QSAR Comb. Sci., 2008, 27(11-12), 1337-1345.
Fourches, D.; Muratov, E.; Tropsha, A. Curation of chemogenomics data. Nat. Chem. Biol., 2015, 11(8), 535.
Fourches, D.; Muratov, E.; Tropsha, A. Trust, but Verify II: A practical guide to chemogenomics data curation. J. Chem. Inf. Model., 2016, 56(7), 1243-1252.
[] [PMID: 27280890]
Fourches, D.; Muratov, E.; Tropsha, A. Trust, but verify: On the importance of chemical structure curation in cheminformatics and QSAR modeling research. J. Chem. Inf. Model., 2010, 50(7), 1189-1204.
[] [PMID: 20572635]
Oecd Principles For The Validation, For Regulatory Purposes, Of (Quantitative) Structure-Activity Relationship Models..
Mueller, R.; Dawson, E.S.; Meiler, J.; Rodriguez, A.L.; Chauder, B.A.; Bates, B.S. Discovery of 2-(2-Benzoxazoyl amino)-4-Aryl-5-Cyanopyrimidine as Negative Allosteric Modulators (NAMs) of Metabotropic Glutamate Receptor 5 (mGlu 5): From an artificial neural network virtual screen to an in vivo tool compound. Chem-Med.Chem., 2012, 7(3), 406-414.
Thorne, N.; Auld, D.S.; Inglese, J. Apparent activity in high throughput screening: Origins of compound-dependent assay interference. Curr. Opin. Chem. Biol., 2010, 14(3), 315-324.
Butkiewicz, M.; Lowe, E.W.; Mueller, R.; Mendenhall, J.L.; Teixeira, P.L.; Weaver, C.D. Benchmarking ligand-based virtual High-Throughput Screening with the PubChem database. Molecules, 2013, 18(1), 735-756.
Sobhy, M.K.; Mowafy, S.; Lasheen, D.S.; Farag, N.A.; Abouzid, K.A.M. 3D-QSAR pharmacophore modelling, virtual screening and docking studies for lead discovery of a novel scaffold for VEGFR 2 inhibitors: Design, synthesis and biological evaluation. Bioorg. Chem., 2019, 89,
Melo-Filho, C.C.; Braga, R.C.; Muratov, E.N.; Franco, C.H.; Moraes, C.B.; Freitas-Junior, L.H. Discovery of new potent hits against intracellular Trypanosoma cruzi by QSAR-based virtual screening. Eur. J. Med. Chem., 2019, 163,
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.
[] [PMID: 30149308]
Kong, Y.; Bender, A.; Yan, A. identification of novel aurora kinase a (aurka) inhibitors via hierarchical ligand-based virtual screening. J. Chem. Inf. Model., 2018, 58(1),
Wen, M.; Deng, Z.; Jiang, S.; Guan, Y.; Wu, H.; Wang, X. Identification of a novel Bcl-2 inhibitor by ligand-based screening and investigation of its anti-cancer effect on human breast cancer cells. Front. Pharmacol., 2019, 10, 391.
Lu, P.; Wang, Y.; Ouyang, P.; She, J.; He, M. 3d-qsar based pharmacophore modeling and virtual screening for identification of novel g protein-coupled receptor40 agonists. Curr. Comput. Aided-Drug Des., 2015, 11(1),
Gorobets, N.Y.; Sedash, Y.V.; Singh, B.K. Poonam, Rathi B. An overview of currently available antimalarials. Curr. Top. Med. Chem., 2017.
Menard, D.; Dondorp, A. Antimalarial drug resistance: A threat to malaria elimination. Cold Spring Harb. Perspect. Med., 2017, 7(7), a025619.
[] [PMID: 28289248]
Penzo, M.; de las Heras-Dueña, L.; Mata-Cantero, L.; Diaz-Hernandez, B.; Vazquez-Muñiz, M-J.; Ghidelli-Disse, S. High throughput screening of the Plasmodium falciparum cGMP dependent protein kinase identified a thiazole scaffold which kills erythrocytic and sexual stage parasites. Sci. Rep., 2019, 9(1), 7005.
Lima, M.N.N.; Melo-Filho, C.C.; Cassiano, G.C.; Neves, B.J.; Alves, V.M.; Braga, R.C.; Cravo, P.V.L.; Muratov, E.N.; Calit, J.; Bargieri, D.Y.; Costa, F.T.M.; Andrade, C.H. QSAR-Driven design and discovery of novel compounds with antiplasmodial and transmission blocking activities. Front. Pharmacol., 2018, 9, 146.
[] [PMID: 29559909]
Colley, D.G.; Bustinduy, A.L.; Secor, W.E.; King, C.H. Human schistosomiasis. Lancet (London, England), 2014, 383, 2253-64.
Kuntz, A.N.; Davioud-Charvet, E.; Sayed, A.A.; Califf, L.L.; Dessolin, J.; Arnér, E.S.J.; Williams, D.L. Thioredoxin glutathione reductase from Schistosoma mansoni: An essential parasite enzyme and a key drug target. PLoS Med., 2007, 4(6), e206.
[] [PMID: 17579510]
Neves, B.J.; Dantas, R.F.; Senger, M.R.; Melo-Filho, C.C.; Valente, W.C.G.; de Almeida, A.C.M. discovery of new anti schistosomal hits by integration of qsar-based virtual screening and high content screening. J. Med. Chem., 2016, 59(15),
Melo-Filho, C.C.; Dantas, R.F.; Braga, R.C.; Neves, B.J.; Senger, M.R.; Valente, W.C.G.; Rezende-Neto, J.M.; Chaves, W.T.; Muratov, E.N.; Paveley, R.A.; Furnham, N.; Kamentsky, L.; Carpenter, A.E.; Silva-Junior, F.P.; Andrade, C.H. QSAR-Driven discovery of novel chemical scaffolds active against Schistosoma mansoni. J. Chem. Inf. Model., 2016, 56(7), 1357-1372.
[] [PMID: 27253773]
Janardhan, S.; John, L.; Prasanthi, M.; Poroikov, V.; Narahari Sastry, G.A QSAR and molecular modelling study towards new lead finding: Polypharmacological approach to Mycobacterium tuberculosis. SAR QSAR Environ. Res., 2017, 28(10), 815-832.
[] [PMID: 29183232]
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),

Rights & Permissions Print Export Cite as
© 2023 Bentham Science Publishers | Privacy Policy