Current computer-aided drug design methodologies in discovery of novel drug candidates for neuropsychiatric and inflammatory diseases

  • Milica Radan University of Belgrade - Faculty of Pharmacy, Department of Pharmaceutical Chemistry
  • Jelena Bošković University of Belgrade - Faculty of Pharmacy, Department of Pharmaceutical Chemistry
  • Vladimir Dobričić University of Belgrade - Faculty of Pharmacy, Department of Pharmaceutical Chemistry
  • Olivera Čudina University of Belgrade - Faculty of Pharmacy, Department of Pharmaceutical Chemistry
  • Katarina Nikolić University of Belgrade-Faculty of Pharmacy, Department of Pharmaceutical Chemistry
Keywords: CADD, 5-HT2A, D2, COX-2, 5-LOX


Drug discovery and development is a very challenging, expensive and time-consuming process. Impressive technological advances in computer sciences and molecular biology have made it possible to use computer-aided drug design (CADD) methods in various stages of the drug discovery and development pipeline. Nowadays, CADD presents an efficacious and indispensable tool, widely used in medicinal chemistry, to lead rational drug design and synthesis of novel compounds. In this article, an overview of commonly used CADD approaches from hit identification to lead optimization was presented. Moreover, different aspects of design of multi-target ligands for neuropsychiatric and anti-inflammatory diseases were summarized. Apparently, designing multi-target directed ligands for treatment of various complex diseases may offer better efficacy, and fewer side effects. Antipsychotics that act through aminergic G protein-coupled receptors (GPCRs), especially dopamine D2 and serotonin 5-HT2A receptors, are the best option for treatment of various symptoms associated with neuropsychiatric disorders. Furthermore, multi-target directed cyclooxygenase-2 (COX-2) and 5-lipoxygenase (5-LOX) inhibitors are also a successful approach to aid the discovery of new anti-inflammatory drugs with fewer side effects. Overall, employing CADD approaches in the process of rational drug design provides a great opportunity for future development, allowing rapid identification of compounds with the optimal polypharmacological profile.


Mohs RC, Greig NH. Drug discovery and development: Role of basic biological research. Alzheimers Dement (N Y). 2017;3(4):651-657.

Hughes JP, Rees S, Kalindjian SB, Philpott KL. Principles of early drug discovery. Br J Pharmacol. 2011;162(6):1239-49.

Kiriiri GK, Njogu PM, Mwangi AN. Exploring different approaches to improve the success of drug discovery and development projects: a review. Futur J Pharm Sci. 2020;6:27.

Sinha S, Vohora D. Chapter 2 – Drug Discovery and Development: An Overview. In: Vohora D, Singh G, editors. Pharmaceutical Medicine and Translational Clinical Research: Drug Discovery and Development. Elsevier; 2018; 19–32 p.

Kapetanovic IM. Computer-aided drug discovery and development (CADDD): in silico-chemico-biological approach. Chem Biol Interact. 2008;171(2):165-176.

Lin X, Li X, Lin X. A Review on Applications of Computational Methods in Drug Screening and Design. Molecules. 2020;25(6),1375.

Bisht N, Singh BK: Role of computer aided drug design in drug development and drug discovery. Int J Pharm Sci Res. 2018;9(4):1405-15.

Surabhi S, Singh BK, Computer Aided Drug Design: An Overview. JDDT. 2018;8(5):504-509.

Sliwoski G, Kothiwale S, Meiler J, Lowe EW. Computational methods in drug discovery. Pharmacol. Rev. 2014;66(1):334–395.

Baig MH, Ahmad K, Rabbani G, Danishuddin M, Choi I. Computer Aided Drug Design and its Application to the Development of Potential Drugs for Neurodegenerative Disorders. Curr Neuropharmacol. 2018;16(6):740-8.

Nikolic K, Mavridis L, Djikic T, Vucicevic J, Agbaba D, Yelekci K, et al. Drug Design for CNS Diseases: Polypharmacological Profiling of Compounds Using Cheminformatic, 3D-QSAR and Virtual Screening Methodologies. Front Neurosci. 2016;10:265.

Baldi A. Computational approaches for drug design and discovery: An overview. Sys Rev Pharm. 2010;1(1):99.

Kore PP, Mutha MM, Antre RV, Oswal RJ, Kshirsagar SS. Computer-Aided Drug Design: An Innovative Tool for Modeling. OJMC. 2012;02(04):139–48.

Yu W, MacKerell AD Jr. Computer-Aided Drug Design Methods. Methods Mol Biol. 2017;1520:85-106.

Leelananda SP, Lindert S. Computational methods in drug discovery. Beilstein J. Org. Chem. 2016;12:2694–2718.

Ferreira LG, Dos Santos RN, Oliva G, Andricopulo AD. Molecular Docking and Structure-Based Drug Design Strategies. Molecules. 2015;20(7):13384-13421.

Berman, H.M. The protein data bank. Nucleic Acids Res. 2000;28:235–42.

Batool M, Ahmad B, Choi S. A Structure-Based Drug Discovery Paradigm. Int. J. Mol. Sci. 2019;20(11):2783.

Vyas VK, Ukawala RD, Ghate M, Chintha C. Homology modeling a fast tool for drug discovery: current perspectives. Indian J Pharm Sci. 2012;74(1):1-17.

Sali A, Blundell TL. Comparative protein modelling by satisfaction of spatial restraints. J Mol Biol. 1993;234(3):779-815.

Biasini M, Bienert S, Waterhouse A, Arnold K, Studer G, Schmidt T, Kiefer F, Gallo Cassarino T, Bertoni M, Bordoli L, Schwede T. SWISS-MODEL: modelling protein tertiary and quaternary structure using evolutionary information. Nucleic Acids Res. 2014;42(Web Server issue):W252-8.

Kalyaanamoorthy S, Chen Y-PP. Structure-based drug design to augment hit discovery. Drug Discov. Today. 2011;16(17-18):831–9.

Salo-Ahen OMH, Alanko I, Bhadane R, Bonvin AMJJ, Honorato RV, Hossain S, et al. Molecular Dynamics Simulations in Drug Discovery and Pharmaceutical Development. Processes. 2021;9(1):71.

Durrant JD, McCammon JA. Molecular dynamics simulations and drug discovery. BMC Biology. 2011;9(1):71.

Hollingsworth SA, Dror RO. Molecular Dynamics Simulation for All. Neuron. 2018;19;99(6):1129-1143.

Michael P. Allen. Introduction to molecular dynamics simulation. Computational soft matter: from synthetic polymers to proteins. 2004;23:1–27.

Hospital A, Goñi JR, Orozco M, Gelpí JL. Molecular dynamics simulations: advances and applications. Adv Appl Bioinform Chem. 2015;8:37-47.

Mura C, McAnany CE. An introduction to biomolecular simulations and docking. Mol Simul. 2014;40(10-11):732–64.

Anandakrishnan R, Drozdetski A, Walker RC, Onufriev AV. Speed of conformational change: comparing explicit and implicit solvent molecular dynamics simulations. Biophys J. 2015;108(5):1153-64.

Case DA, Cheatham TE 3rd, Darden T, Gohlke H, Luo R, Merz KM Jr, et al. The AMBER biomolecular simulation programs. J Comput Chem 2005;26:1668-88.

Brooks BR, Bruccoleri RE, Olafson BD, States DJ, Swaminathan S, Karplus M. CHARMM - a program for macromolecular energy, minimization, and dynamics calculations. J Comput Chem. 1983;4:187-217.

Christen M, Hünenberger PH, Bakowies D, Baron R, Bürgi R, Geerke DP, et al. The GROMOS software for biomolecular simulation: GROMOS05. J Comput Chem. 2005;26:1719-51.

Phillips JC, Braun R, Wang W, Gumbart J, Tajkhorshid E, Villa E, et al. Scalable molecular dynamics with NAMD. J Comput Chem. 2005;26:1781–1802.

Forli S, Huey R, Pique ME, Sanner MF, Goodsell DS, Olson AJ. Computational protein–ligand docking and virtual drug screening with the AutoDock suite. Nat Protoc. 2016;11(5):905–19.

Meza Menchaca T, Juárez-Portilla C, C. Zepeda R. Past, Present, and Future of Molecular Docking. In: Gaitonde V, Karmakar P, Trivedi A, editors. Drug Discovery and Development - New Advances. IntechOpen; 2020.

Morris GM, Lim-Wilby M. Molecular docking. Methods Mol Biol. 2008;443:365-82.

Salmaso V, Moro S. Bridging Molecular Docking to Molecular Dynamics in Exploring Ligand-Protein Recognition Process: An Overview. Front Pharmacol. 2018;9:923.

Sethi A, Joshi K, Sasikala K, Alvala M. Molecular Docking in Modern Drug Discovery: Principles and Recent Applications. In: Gaitonde V, Karmakar P, Trivedi A, editors. Drug Discovery and Development - New Advances. IntechOpen; 2020.

Fan J, Fu A, Zhang L. Progress in molecular docking. Quant. Biol. 2019;7(2):83‒9.

Meng XY, Zhang HX, Mezei M, Cui M. Molecular docking: a powerful approach for structure-based drug discovery. Curr Comput Aided Drug Des. 2011;7(2):146-57.

Morris GM, Huey R, Lindstrom W, Sanner MF, Belew RK,Goodsell DS, et al. AutoDock4 and AutoDockTools4: automateddocking with selective receptor flexibility. J Comput Chem. 2009;30:2785–91

Trott O, Olson AJ. AutoDock Vina: improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading. J Comput Chem. 2010;31(2):455-61.

Verdonk ML, Cole JC, Hartshorn MJ, Murray CW, Taylor RD. Improved protein-ligand docking using GOLD. Proteins. 2003;52:609–23

Halgren TA, Murphy RB, Friesner RA, Beard HS, Frye LL, Pollard WT, et al. Glide: a new approach for rapid, accurate docking and scoring.2. Enrichment factors in database screening. J Med Chem. 2004;47:1750–59

Boittier ED, Tang YY, Buckley ME, Schuurs ZP, Richard DJ, Gandhi NS. Assessing Molecular Docking Tools to Guide Targeted Drug Discovery of CD38 Inhibitors. IJMS. 2020;21(15):5183.

Pagadala NS, Syed K, Tuszynski J. Software for molecular docking: a review. Biophys Rev. 2017;9(2):91-102.

Shoichet BK. Virtual screening of chemical libraries. Nature. 2004;432(7019): 862–5.

Banegas-Luna AJ, Cerón-Carrasco JP, Pérez-Sánchez H. A review of ligand-based virtual screening web tools and screening algorithms in large molecular databases in the age of big data. Future Med Chem. 2018;10(22):2641-2658.

Singh N, Chaput L, Villoutreix BO. Virtual screening web servers: designing chemical probes and drug candidates in the cyberspace. Brief Bioinform. 2021; 22(2):1790-1818.

Lyne PD. Structure-based virtual screening: an overview. Drug Discov Today. 2002; 7(20):1047-55.

Maia EHB, Assis LC, de Oliveira TA, da Silva AM, Taranto AG. Structure-Based Virtual Screening: From Classical to Artificial Intelligence. Front Chem. 2020;28;8:343.

Verma J, Khedkar VM, Coutinho EC. 3D-QSAR in drug design--a review. Curr Top Med Chem. 2010;10(1):95-115.

Cherkasov A, Muratov EN, Fourches D, Varnek A, Baskin II, Cronin M, et al. QSAR modeling: where have you been? Where are you going to? J Med Chem. 2014;57(12):4977-5010.

K. Roy, S. Kar, R. N. Das. A primer on QSAR/QSPR modeling, Springer, Cham. 2015, (pp. 37-59)

Aguayo-Ortiz R, Fernández-de Gortari E. Overview of computer-aided drug design for epigenetic targets. In MedinaFranco JL, editors. Epi-Informatics. 2016; p. 21–52.

Dobričić V, Jaćević V, Vučićević J, Nikolic K, Vladimirov S, Čudina O. Evaluation of Biological Activity and Computer-Aided Design of New Soft Glucocorticoids. Arch Pharm (Weinheim). 2017;350(5).

Golbraikh A, Tropsha A. Beware of q2! J Mol Graph Model. 2002;20(4):269-76

van de Waterbeemd H, Gifford E. ADMET in silico modelling: towards prediction paradise? Nat Rev Drug Discov. 2003;2(3):192-204.

Krüger A, Maltarollo VG, Wrenger C, Kronenberger T. ADME Profiling in Drug Discovery and a New Path Paved on Silica. In: Gaitonde V, Karmakar P, Trivedi A, editors. Drug Discovery and Development - New Advances. IntechOpen; 2020.

Vucicevic J, Nikolic K, Dobričić V, Agbaba D. Prediction of blood-brain barrier permeation of α-adrenergic and imidazoline receptor ligands using PAMPA technique and quantitative-structure permeability relationship analysis. Eur J Pharm Sci. 2015 Feb 20;68:94-105.

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 Mar 3;7:42717.

ADMET Predictor v 9.5, n.d. Simulations plus inc., lancaster, CA, USA https://www.simulationsplus

QikProp, Schrödinger, LLC, NY, 2019,

Michino M, Beuming T, Donthamsetti P, Newman AH, Javitch JA, Shi L. What Can Crystal Structures of Aminergic Receptors Tell Us about Designing Subtype-Selective Ligands? Pharmacol Rev. 2015;67(1):198-213.

Choudhury A, Sahu T, Ramanujam PL, Kumar Banerjee A, Chakraborty I, Kumar RA, et al. Neurochemicals, Behaviours and Psychiatric Perspectives of Neurological Diseases. Neuropsychiatry. 2018;8(1):395–424.

Vangveravong S, McElveen E, Taylor M, Xu J, Tu Z, Luedtke RR, et al. Synthesis and characterization of selective dopamine D2 receptor antagonists. Bioorg Med Chem. 2006;14(3):815-25.

Kapur S, Mamo D. Half a century of antipsychotics and still a central role for dopamine D2 receptors. Prog Neuropsychopharmacol Biol Psychiatry. 2003;27(7):1081-90.

Raote I, Bhattacharya A, Panicker MM. Serotonin 2A (5-HT2A) Receptor Function: Ligand-Dependent Mechanisms and Pathways. In: Chattopadhyay A, editor. Serotonin Receptors in Neurobiology. Boca Raton (FL): CRC Press/Taylor & Francis; 2007. Chapter 6.

Grinchii D, Dremencov E. Mechanism of Action of Atypical Antipsychotic Drugs in Mood Disorders. Int J Mol Sci. 2020;21(24):9532.

Worrel JA, Marken PA, Beckman SE, Ruehter VL: Atypical antipsychotic agents: a critical review. Am J Health Syst Pharm. 2000;57:238–55.

Butini S, Nikolic K, Kassel S, Brückmann H, Filipic S, Agbaba D, et al. Polypharmacology of dopamine receptor ligands. Prog Neurobiol. 2016;142:68-103.

Munk C, Isberg V, Mordalski S, Harpsøe K, Rataj K, Hauser AS, et al. GPCRdb: the G protein‐coupled receptor database – an introduction. Br J Pharmacol. 2016;173(14):2195-207.

Vass M, Podlewska S, De Esch IJP, Bojarski AJ, Leurs R, Kooistra AJ, et al. Aminergic GPCR-ligand interactions: A chemical and structural map of receptor mutation data. J Med Chem. 2019;25;62(8):3784-3839.

Oluić J, Nikolic K, Vucicevic J, Gagic Z, Filipic S, Agbaba D. 3D-QSAR, Virtual Screening, Docking and Design of Dual PI3K/mTOR Inhibitors with Enhanced Antiproliferative Activity. Comb Chem High Throughput Screen. 2017;20(4):292-303.

Gagic Z, Ruzic D, Djokovic N, Djikic T, Nikolic K. In silico Methods for Design of Kinase Inhibitors as Anticancer Drugs. Front Chem. 2020;7:873.

Ísberg V, Balle T, Sander, T, Jørgensen FS, Gloriam DE. G Protein- and Agonist-Bound Serotonin 5-HT2A Receptor Model Activated by Steered Molecular Dynamics Simulations. J Chem Inf Model. 2011;51:315–25.

Platania CB, Salomone S, Leggio GM, Drago F, Bucolo C. Homology modeling of dopamine D2 and D3 receptors: molecular dynamics refinement and docking evaluation. PLoS One. 2012;7(9):e44316.

Kim K, Che T, Panova O, DiBerto JF, Lyu J, Krumm BE, et al. Structure of a Hallucinogen-Activated Gq-Coupled 5-HT2A Serotonin Receptor. Cell. 2020;182(6):1574-1588.e19.

Kimura KT, Asada H, Inoue A, Kadji FMN, Im D, Mori C, et al. Structures of the 5-HT2A receptor in complex with the antipsychotics risperidone and zotepine. Nat Struct Mol Biol. 2019;26(2):121-8.

Zhuang Y, Xu P, Mao C, Wang L, Krumm B, Zhou XE, et al. Structural insights into the human D1 and D2 dopamine receptor signaling complexes. Cell. 2021;184(4):931-942.e18.

Im D, Inoue A, Fujiwara T, Nakane T, Yamanaka Y, Uemura T, et al. Structure of the dopamine D2 receptor in complex with the antipsychotic drug spiperone. Nat Commun. 2020;11(1):6442.

Yin J, Chen KM, Clark MJ, Hijazi M, Kumari P, Bai XC, et al. Structure of a D2 dopamine receptor-G-protein complex in a lipid membrane. Nature. 2020;584(7819):125-9.

Fan L, Tan L, Chen Z, Qi J, Nie F, Luo Z, et al. Haloperidol bound D2 dopamine receptor structure inspired the discovery of subtype selective ligands. Nat Commun. 2020;11(1):1074.

Wang S, Che T, Levit A, Shoichet BK, Wacker D, Roth BL. Structure of the D2 dopamine receptor bound to the atypical antipsychotic drug risperidone. Nature. 2018;555(7695):269-73.

Lin F, Li F, Wang C, Wang J, Yang Y, Yang L, et al. Mechanism Exploration of Arylpiperazine Derivatives Targeting the 5-HT2A Receptor by In Silico Methods. Molecules. 2017;22(7):1064.

Zhang C, Li Q, Meng L, Ren Y. Design of novel dopamine D2 and serotonin 5-HT2A receptors dual antagonists toward schizophrenia: An integrated study with QSAR, molecular docking, virtual screening and molecular dynamics simulations. J Biomol Struct Dyn. 2020;38(3):860-85.

SYBYL-X. (Tripos International: 1699 South Hanley Road, St. Louis, MO 63144-2319, USA).

Irwin JJ, Sterling T, Mysinger MM, Bolstad ES, Coleman RG. ZINC: a free tool to discover chemistry for biology. J Chem Inf Model. 2012;52(7):1757-68.

Kumar R, Jade D, Gupta D. A novel identification approach for discovery of 5-HydroxyTriptamine 2A antagonists: combination of 2D/3D similarity screening, molecular docking and molecular dynamics. J Biomol Struct Dyn. 2019 Mar;37(4):931-43.

Kaczor AA, Silva AG, Loza MI, Kolb P, Castro M, Poso A. Structure-Based Virtual Screening for Dopamine D2 Receptor Ligands as Potential Antipsychotics. ChemMedChem. 2016; 11, 718–29.

Small-Molecule Drug Discovery Suite 2015-4: Glide, version 6.9, Schrçdinger, LLC, New York, NY (USA), 2015;

Kaczor AA, Targowska-Duda KM, Budzyńska B, Biała G, Silva AG, Castro M. In vitro, molecular modeling and behavioral studies of 3-{[4-(5-methoxy-1H-indol-3-yl)-1,2,3,6-tetrahydropyridin-1-yl]methyl}-1,2-dihydroquinolin-2-one (D2AAK1) as a potential antipsychotic. Neurochem Int. 2016;96:84-99.

Kondej M, Wróbel TM, Silva AG, Stępnicki P, Koszła O, Kędzierska E, et al. Synthesis, pharmacological and structural studies of 5-substituted-3-(1-arylmethyl-1,2,3,6-tetrahydropyridin-4-yl)-1H-indoles as multi-target ligands of aminergic GPCRs. Eur J Med Chem. 2019;180:673-89.

Koszła O, Sołek P, Woźniak S, Kędzierska E, Wróbel TM, Kondej M, et al. The Antipsychotic D2AAK1 as a Memory Enhancer for Treatment of Mental and Neurodegenerative Diseases. Int J Mol Sci. 2020;21(22):8849.

Maia EHB, Assis LC, de Oliveira TA, da Silva AM, Taranto AG. Structure-Based Virtual Screening: From Classical to Artificial Intelligence. Front Chem. 2020;8:343.

Zięba A, Żuk J, Bartuzi D, Matosiuk D, Poso A, Kaczor AA. The Universal 3D QSAR Model for Dopamine D2 Receptor Antagonists. Int J Mol Sci. 2019;20(18):4555.

Kanagarajadurai K, Malini M, Bhattacharya A, Panicker MM, Sowdhamini R. Molecular modeling and docking studies of human 5-hydroxytryptamine 2A (5-HT2A) receptor for the identification of hotspots for ligand binding. Mol Biosyst. 2009 Dec;5(12):1877-88.

Radan M, Ruzic D, Antonijevic M, Djikic T, Nikolic K. In silico identification of novel 5-HT2A antagonists supported with ligand- and target-based drug design methodologies. J Biomol Struct Dyn. 2021;39(5):1819-37.

Pastor M, McLay I, Pickett S, Clementi S. Grid-Independent descriptors (GRIND): a novel class of alignment-independent three-dimensional molecular descriptors. Med Chem. 2000;43(17):3233-3243.

Zaręba P, Jaśkowska J, Śliwa P, Satała G. New dual ligands for the D2 and 5-HT1A receptors from the group of 1,8-naphthyl derivatives of LCAP. Bioorg Med Chem Lett. 2019;29(16):2236-2242.

Le François B, Zhang L, Mahajan GJ, Stockmeier CA, Friedman E, Albert PR. A Novel Alternative Splicing Mechanism That Enhances Human 5-HT1A Receptor RNA Stability Is Altered in Major Depression. J Neurosci. 2018;38(38):8200-8210.

Zhu C, Li X, Zhao B, Peng W, Li W, Fu W. Discovery of aryl-piperidine derivatives as potential antipsychotic agents using molecular hybridization strategy. Eur J Med Chem. 2020;193:112214.

Xu L, Zhou S, Yu K, Gao B, Jiang H, Zhen X, et al. Molecular modeling of the 3D structure of 5-HT(1A)R: discovery of novel 5-HT(1A)R agonists via dynamic pharmacophore-based virtual screening. J Chem Inf Model. 2013;53(12):3202-11.

Shi W, Wang Y, Wu C, Yang F, Zheng W, Wu S, et al. Synthesis and biological investigation of triazolopyridinone derivatives as potential multireceptor atypical antipsychotics. Bioorg Med Chem Lett. 2020;30(8):127027.

Greenwood J, Acharya RB, Marcellus V, Rey JA. Lumateperone: A Novel Antipsychotic for Schizophrenia. Ann Pharmacother. 2021;55(1):98-104.

Koblan KS, Kent J, Hopkins SC, Krystal JH, Cheng H, Goldman R, et al. A Non-D2-Receptor-Binding Drug for the Treatment of Schizophrenia. N Engl J Med. 2020;382(16):1497-1506.

Dedic N, Jones PG, Hopkins SC, Lew R, Shao L, Campbell JE, et al. SEP-363856, a Novel Psychotropic Agent with a Unique, Non-D2 Receptor Mechanism of Action. J Pharmacol Exp Ther. 2019;371(1):1-14.

Dodd S, F Carvalho A, Puri BK, Maes M, Bortolasci CC, Morris G, et al. Trace Amine-Associated Receptor 1 (TAAR1): A new drug target for psychiatry? Neurosci Biobehav Rev. 2021;120:537-541.

Orlando BJ, Malkowski MG. Substrate-selective Inhibition of Cyclooxygeanse-2 by Fenamic Acid Derivatives Is Dependent on Peroxide Tone. J Biol Chem. 2016;291(29):15069-81.

Kurumbail RG, Stevens AM, Gierse JK, McDonald JJ, Stegeman RA, Pak JY, Gildehaus D, Miyashiro JM, Penning TD, Seibert K, Isakson PC, Stallings WC. Structural basis for selective inhibition of cyclooxygenase-2 by anti-inflammatory agents. Nature. 1996;384(6610):644-8.

Gilbert NC, Bartlett SG, Waight MT, Neau DB, Boeglin WE, Brash AR, et al. The structure of human 5-lipoxygenase. Science. 2011;331(6014):217-9.

Gilbert NC, Rui Z, Neau DB, Waight MT, Bartlett SG, Boeglin WE, et al. Conversion of human 5-lipoxygenase to a 15-lipoxygenase by a point mutation to mimic phosphorylation at Serine-663. FASEB J. 2012;26(8):3222-9.

P JJ, Manju SL, Ethiraj KR, Elias G. Safer anti-inflammatory therapy through dual COX-2/5-LOX inhibitors: A structure-based approach. Eur J Pharm Sci. 2018;121:356-381.

Weisman SM, Doyle MJ, Wehmeyer KR, Hynd BA, Eichhold TH, Clear RM, et al. Effects of tebufelone (NE-11740), a new anti-inflammatory drug, on arachidonic acid metabolism. Agents Actions, 1994;41: 156-163.

Ruiz J, Pérez C, Pouplana R. QSAR study of dual cyclooxygenase and 5-lipoxygenase inhibitors 2,6-di-tert-butylphenol derivatives. Bioorg Med Chem. 2003;11(19):4207-16.

Unangst PC, Connor DT, Cetenko WA, Sorenson RJ, Kostlan CR, Sircar JC et al. Synthesis and biological evaluation of 5-[[3,5-bis(1,1-dimethylethyl)- 4-hydroxyphenyl]methylene]oxazoles, -thiazoles, and -imidazoles: novel dual 5-lipoxygenase and cyclooxygenase inhibitors with antiinflammatory activity. J. Med. Chem. 1994;37:322-328.

Inagaki M, Tsuri T, Jyoyama H, Ono T, Yamada K, Kobayashi M, et al. Novel antiarthritic agents with 1,2-isothiazolidine1,1-dioxide (gamma-sultam) skeleton: cytokine suppressive dual inhibitors of cyclooxygenase-2 and 5-lipoxygenase. J. Med. Chem. 2000;43:2040-2048

Hwang SH, Wecksler AT, Wagner K, Hammock BD. Rationally designed multitarget agents against inflammation and pain. Curr Med Chem. 2013;20(13):1783-99.

Ghatak S, Vyas A, Misra S, O'Brien P, Zambre A, Fresco VM, et al. Novel di-tertiary-butyl phenylhydrazones as dual cyclooxygenase-2/5-lipoxygenase inhibitors: synthesis, COX/LOX inhibition, molecular modeling, and insights into their cytotoxicities. Bioorg Med Chem Lett. 2014;24(1):317-24.

Lino RC, da Silva DPB, Florentino IF, da Silva DM, Martins JLR, Batista DDC, et al. Pharmacological evaluation and molecular docking of new di-tert-butylphenol compound, LQFM-091, a new dual 5-LOX/COX inhibitor. Eur J Pharm Sci. 2017;106:231-243.

Hanwell MD, Curtis DE, Lonie DC, Vandermeersch T, Zurek E, Hutchison GR. “Avogadro: An advanced semantic chemical editor, visualization, and analysis platform”. J Cheminform. 2012;4:17.

Frisch MJ, Trucks GW, Schlegel HB, Scuseria GE, Robb MA, Cheeseman JR, et al. Gaussian 09. Gaussian, Inc., Wallingford CT. 2009.

de Magalhães CS, Almeida DM, Barbosa HJC, Dardenne LE. A dynamic niching genetic algorithm strategy for docking highly flexible ligands. Inform. Sci. 2014;289:206–224.

Laskowski RA, Swindells MB. LigPlot+: multiple ligand-protein interaction diagrams for drug discovery. J. Chem. Inf. Model. 2011;51:2778–2786.

Janusz JM, Young PA, Ridgeway JM, Scherz MW, Enzweiler K, Wu LI, et al. New cyclooxygenase-2/5-lipoxygenase inhibitors. 1. 7-tert-buty1-2,3-dihydro-3,3-dimethylbenzofuran derivatives as gastrointestinal safe antiinflammatory and analgesic agents: discovery and variation of the 5-keto substituent. J Med Chem. 1998;41(7):1112-23.

Zheng M, Zhang Z, Zhu W, Liu H, Luo X, Chen K, et al. Essential structural profile of a dual functional inhibitor against cyclooxygenase-2 (COX-2) and 5-lipoxygenase (5-LOX): molecular docking and 3D-QSAR analyses on DHDMBF analogues. Bioorg Med Chem. 2006;14(10):3428-37.

Sali A. Comparative protein modeling by satisfaction of spatial restraints. Mol Med Today. 1995;1(6):270-7.

Misra S, Ghatak S, Patil N, Dandawate P, Ambike V, Adsule S, et al. Novel dual cyclooxygenase and lipoxygenase inhibitors targeting hyaluronan-CD44v6 pathway and inducing cytotoxicity in colon cancer cells. Bioorg Med Chem. 2013;21(9):2551-9.

Frisch MJ, Trucks GW, Schlegel HB, Gill PMW, Johnson BG, Robb MA, et al. GAUSSIAN 94, Rev. E.1; Gaussian Inc.: Pittsburgh, PA, 1995.

Sanz F, Manaut F, Rodríguez J, Lozoya E, López-de-Briñas E. MEPSIM: a computational package for analysis and comparison of molecular electrostatic potentials. J Comput Aided Mol Des. 1993;7(3):337-47.

Pommery N, Taverne T, Telliez A, Goossens L, Charlier C, Pommery J, et al. New COX-2/5-LOX inhibitors: apoptosis-inducing agents potentially useful in prostate cancer chemotherapy. J Med Chem. 2004;47(25):6195-206.

Revathi S, Gupta AK, Soni LK, Kavitha S, Wagh R, Kaskhedikar SG. Rationalization of physicochemical characters of 1,5-diarylpyrazole analogs as dual (COX-2/LOX-5) inhibitors: a QSAR approach. J Pharm Biomed Anal. 2006;42(2):283-9.

CS Chem Office, Version 8.0, Cambridge Soft Corporation, Software Publishers Association, 1730 M Street, Suite 700, Washington, DC 20036, USA.

Todeschini R, Consonni V. DRAGON-Software for the Calculation of Molecular Descriptors, rel. 1.12 for Windows, 2001.

Gupta AK, Babu MA, Kaskhedikar SG.VALSTAT: validation program for quantitative structure activity relationship studies. Indian J Pharm Sci. 2004;66:396–402.

Liaras K, Fesatidou M, Geronikaki A. Thiazoles and Thiazolidinones as COX/LOX Inhibitors. Molecules. 2018;23(3):685.

Geronikaki AA, Lagunin AA, Hadjipavlou-Litina DI, Eleftheriou PT, Filimonov DA, Poroikov VV, et al. Computer-aided discovery of anti-inflammatory thiazolidinones with dual cyclooxygenase/lipoxygenase inhibition. J Med Chem. 2008;51(6):1601-9.

Lagunin A, Stepanchikova A, Filimonov D, Poroikov V. PASS: prediction of activity spectra for biologically active substances. Bioinformatics. 2000;16(8):747-8.

Poroikov VV, Filimonov DA, Gloriozova TA, Lagunin AA, Druzhilovskiy DS, Rudik AV, et al. Computer-aided prediction of biological activity spectra for organic compounds: the possibilities and limitations. Russ Chem Bull. 2019;68:2143–2154.

Kaur J, Bhardwaj A, Huang Z, Knaus EE. Aspirin analogues as dual cyclooxygenase-2/5-lipoxygenase inhibitors: synthesis, nitric oxide release, molecular modeling, and biological evaluation as anti-inflammatory agents. ChemMedChem. 2012;7(1):144-50.

Mark A. ArgusLab version 4.0.1, Thompson Planaria Software LLC, Seattle, WA (USA).

Discovery Studio 3.0, Molecular Modeling Software, Accelrys Inc.

Misra S, Ghatak S, Patil N, Dandawate P, Ambike V, Adsule S, et al. Novel dual cyclooxygenase and lipoxygenase inhibitors targeting hyaluronan-CD44v6 pathway and inducing cytotoxicity in colon cancer cells. Bioorg Med Chem. 2013;21(9):2551-9.

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