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Medicinal Chemistry

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ISSN (Print): 1573-4064
ISSN (Online): 1875-6638

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Research Article

Virtual Screening and Multi-targets Investigation of Novel Diazine Derivatives as Potential Xanthine Oxidase Inhibitors Based on QSAR, Molecular Docking, ADMET Properties, Dynamics Simulation and Network Pharmacology

Author(s): Bingxiang Yi, Jiaying Sun*, Yaru Liu, Zhiping Zhang, Rui Wang, Mao Shu and Zhihua Lin

Volume 19, Issue 7, 2023

Published on: 20 February, 2023

Page: [704 - 716] Pages: 13

DOI: 10.2174/1573406419666230209092231

Price: $65

Abstract

Background: Hyperuricemia is closely related to the occurrence of gout, hypertension, diabetes, hyperlipidemia, cardiovascular disease, kidney disease, metabolic syndrome, etc. However, xanthine oxidase inhibitors (XOIs) can fundamentally solve the problem of excessive uric acid. Compared to single-target drugs, multi-target drugs are not prone to adverse reactions and exert a synergistic effect. Therefore, the discovery of new multi-target XOIs and their mechanism of therapeutic hyperuricemia are important to overcome adverse effects and resistance to currently available drugs.

Objective: The purpose of this paper is to obtain novel diazine derivatives as promising multi-target XOIs and discover the interaction mechanism for the better treatment of hyperuricemia.

Methods: Novel multi-target XOIs diazine derivatives, and their interaction mechanism have been obtained through QSAR, molecular docking, dynamics simulation, and network pharmacology. In addition, ADMET properties and synthetic accessibility of novel XOIs have been considered using ADMETLAB 2.0 and SwissADME.

Results: 24 novel diazine derivatives as potential multi-target XOIs lead compounds have been found through virtual screening of the PubChem database. Moreover, the most notable top five compounds are worthy of further developing as multi-target XOIs drugs. XDH, TBK1, DGAT1, MYC, CDKN1A, PPARD, PDE6C, and EIF4E are recommended as relevant targets of therapeutic hyperuricemia.

Conclusion: Through the combination of different methods, we have discovered five novel promising diazine derivatives as potential multi-target XOIs drugs. Meanwhile, eight targets have been found to be helpful in the research on therapeutic hyperuricemia. We expect this investigation will offer clear insights into the production of efficient XOIs drugs.

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[1]
Ojha, R.; Singh, J.; Ojha, A.; Singh, H.; Sharma, S.; Nepali, K. An updated patent review: xanthine oxidase inhibitors for the treatment of hyperuricemia and gout (2011-2015). Expert Opin. Ther. Pat., 2017, 27(3), 311-345.
[http://dx.doi.org/10.1080/13543776.2017.1261111] [PMID: 27841045]
[2]
Gunawardhana, L.; McLean, L.; Punzi, H.A.; Hunt, B.; Palmer, R.N.; Whelton, A.; Feig, D.I. Effect of febuxostat on ambulatory blood pressure in subjects with hyperuricemia and hypertension: A phase 2 randomized placebo-controlled study. J. Am. Heart Assoc., 2017, 6(11), e006683.
[http://dx.doi.org/10.1161/JAHA.117.006683] [PMID: 29102979]
[3]
Šmelcerović, A.; Tomović, K.; Šmelcerović, Ž.; Petronijević, Ž.; Kocić, G.; Tomašič, T.; Jakopin, Ž.; Anderluh, M. Xanthine oxidase inhibitors beyond allopurinol and febuxostat; an overview and selection of potential leads based on in silico calculated physicochemical properties, predicted pharmacokinetics and toxicity. Eur. J. Med. Chem., 2017, 135, 491-516.
[http://dx.doi.org/10.1016/j.ejmech.2017.04.031] [PMID: 28478180]
[4]
Vickneson, K.; George, J. Xanthine oxidoreductase inhibitors. Handb. Exp. Pharmacol., 2020, 264, 205-228.
[http://dx.doi.org/10.1007/164_2020_383] [PMID: 32789757]
[5]
Strilchuk, L.; Fogacci, F.; Cicero, A.F. Safety and tolerability of available urate-lowering drugs: a critical review. Expert Opin. Drug Saf., 2019, 18(4), 261-271.
[http://dx.doi.org/10.1080/14740338.2019.1594771] [PMID: 30915866]
[6]
Mehmood, A.; Ishaq, M.; Zhao, L.; Safdar, B.; Rehman, A.; Munir, M.; Raza, A.; Nadeem, M.; Iqbal, W.; Wang, C. Natural compounds with xanthine oxidase inhibitory activity: A review. Chem. Biol. Drug Des., 2019, 93(4), 387-418.
[http://dx.doi.org/10.1111/cbdd.13437] [PMID: 30403440]
[7]
Luna, G.; Dolzhenko, A.V.; Mancera, R.L. Inhibitors of xanthine oxidase: scaffold diversity and structure-based drug design. Chem. Med. Chem. , 2019, 14(7), 714-743.
[http://dx.doi.org/10.1002/cmdc.201900034] [PMID: 30740924]
[8]
Fais, A.; Era, B.; Asthana, S.; Sogos, V.; Medda, R.; Santana, L.; Uriarte, E.; Matos, M.J.; Delogu, F.; Kumar, A. Coumarin derivatives as promising xanthine oxidase inhibitors. Int. J. Biol. Macromol., 2018, 120(Pt A), 1286-1293.
[http://dx.doi.org/10.1016/j.ijbiomac.2018.09.001] [PMID: 30189275]
[9]
Santi, M.D.; Paulino Zunini, M.; Vera, B.; Bouzidi, C.; Dumontet, V.; Abin-Carriquiry, A.; Grougnet, R.; Ortega, M.G. Xanthine oxidase inhibitory activity of natural and hemisynthetic flavonoids from Gardenia oudiepe (Rubiaceae) in vitro and molecular docking studies. Eur. J. Med. Chem., 2018, 143, 577-582.
[http://dx.doi.org/10.1016/j.ejmech.2017.11.071] [PMID: 29207340]
[10]
Kong, L.D.; Cai, Y.; Huang, W.W.; Cheng, C.H.K.; Tan, R.X. Inhibition of xanthine oxidase by some Chinese medicinal plants used to treat gout. J. Ethnopharmacol., 2000, 73(1-2), 199-207.
[http://dx.doi.org/10.1016/S0378-8741(00)00305-6] [PMID: 11025157]
[11]
Wang, X.; Zhao, M.; Su, G.; Cai, M.; Zhou, C.; Huang, J.; Lin, L. The antioxidant activities and the xanthine oxidase inhibition effects of walnut ( Juglans regia L.) fruit, stem and leaf. Int. J. Food Sci. Technol., 2015, 50(1), 233-239.
[http://dx.doi.org/10.1111/ijfs.12672]
[12]
Cheng, L.C.; Murugaiyah, V.; Chan, K.L. Flavonoids and phenylethanoid glycosides from Lippia nodiflora as promising antihyperuricemic agents and elucidation of their mechanism of action. J. Ethnopharmacol., 2015, 176, 485-493.
[http://dx.doi.org/10.1016/j.jep.2015.11.025] [PMID: 26593216]
[13]
Alvareda, E.; Iribarne, F.; Espinosa, V.; Miranda, P.; Santi, D.; Aguilera, S.; Bustos, S.; Zunini, M.P. in silico and in vitro approach for the understanding of the xanthine oxidase inhibitory activity of Uruguayan Tanat grape pomace and propolis poliphenols. J. Biophys. Chem., 2019, 10(1), 1-14.
[http://dx.doi.org/10.4236/jbpc.2019.101001 ]
[14]
Dwibedi, V.; Jain, S.; Singhal, D.; Mittal, A.; Rath, S.K.; Saxena, S. Inhibitory activities of grape bioactive compounds against enzymes linked with human diseases. Appl. Microbiol. Biotechnol., 2022, 106(4), 1399-1417.
[http://dx.doi.org/10.1007/s00253-022-11801-9] [PMID: 35106636]
[15]
Rahaman, M.S.; Siraj, M.A.; Islam, M.A.; Shanto, P.C.; Islam, O.; Islam, M.A.; Simal-Gandara, J. Crosstalk between xanthine oxidase (XO) inhibiting and cancer chemotherapeutic properties of comestible flavonoids- a comprehensive update. J. Nutr. Biochem., 2022, 110, 109147.
[http://dx.doi.org/10.1016/j.jnutbio.2022.109147] [PMID: 36049673]
[16]
Guan, Q.; Cheng, Z.; Ma, X.; Wang, L.; Feng, D.; Cui, Y.; Bao, K.; Wu, L.; Zhang, W. Synthesis and bioevaluation of 2-phenyl-4-methyl-1,3-selenazole-5-carboxylic acids as potent xanthine oxidase inhibitors. Eur. J. Med. Chem., 2014, 85, 508-516.
[http://dx.doi.org/10.1016/j.ejmech.2014.08.014] [PMID: 25113879]
[17]
Chen, S.; Zhang, T.; Wang, J.; Wang, F.; Niu, H.; Wu, C.; Wang, S. Synthesis and evaluation of 1-hydroxy/methoxy-4-methyl-2-phenyl-1H-imidazole-5-carboxylic acid derivatives as non-purine xanthine oxidase inhibitors. Eur. J. Med. Chem., 2015, 103, 343-353.
[http://dx.doi.org/10.1016/j.ejmech.2015.08.056] [PMID: 26363870]
[18]
Li, J.; Wu, F.; Liu, X.; Zou, Y.; Chen, H.; Li, Z.; Zhang, L. Synthesis and bioevaluation of 1-phenyl-pyrazole-4-carboxylic acid derivatives as potent xanthine oxidoreductase inhibitors. Eur. J. Med. Chem., 2017, 140, 20-30.
[http://dx.doi.org/10.1016/j.ejmech.2017.08.047] [PMID: 28918097]
[19]
Kaur, M.; Kaur, A.; Mankotia, S.; Singh, H.; Singh, A.; Singh, J.V.; Gupta, M.K.; Sharma, S.; Nepali, K.; Bedi, P.M.S. Synthesis, screening and docking of fused pyrano[3,2- d]pyrimidine derivatives as xanthine oxidase inhibitor. Eur. J. Med. Chem., 2017, 131, 14-28.
[http://dx.doi.org/10.1016/j.ejmech.2017.03.002] [PMID: 28286211]
[20]
Figueiredo, J.; Serrano, J.L.; Cavalheiro, E.; Keurulainen, L.; Yli-Kauhaluoma, J.; Moreira, V.M.; Ferreira, S.; Domingues, F.C.; Silvestre, S.; Almeida, P. Trisubstituted barbiturates and thiobarbiturates: Synthesis and biological evaluation as xanthine oxidase inhibitors, antioxidants, antibacterial and anti-proliferative agents. Eur. J. Med. Chem., 2018, 143, 829-842.
[http://dx.doi.org/10.1016/j.ejmech.2017.11.070] [PMID: 29223098]
[21]
Zhang, T.; Li, S.; Wang, L.; Sun, Q.; Wu, Q.; Zhang, Y.; Meng, F. Design, synthesis and biological evaluation of N-(4-alkoxy-3-cyanophenyl)isonicotinamide/nicotinamide derivatives as novel xanthine oxidase inhibitors. Eur. J. Med. Chem., 2017, 141, 362-372.
[http://dx.doi.org/10.1016/j.ejmech.2017.09.051] [PMID: 29032030]
[22]
Zhang, T.; Zhang, Z.; Zhang, X.; Wang, Z.; Xu, E.; Tu, S.; Zhang, Y.; Meng, F. Design, synthesis and biological evaluation of N-(4-alkoxy-3-(1H-tetrazol-1-yl)phenyl) heterocyclic aromatic amide derivatives as xanthine oxidase inhibitors. Bioorg. Chem., 2022, 127, 105938.
[http://dx.doi.org/10.1016/j.bioorg.2022.105938] [PMID: 35752100]
[23]
Dolezal, M.; Zitko, J. Pyrazine derivatives: a patent review (June 2012 – present). Expert Opin. Ther. Pat., 2015, 25(1), 33-47.
[http://dx.doi.org/10.1517/13543776.2014.982533] [PMID: 25523365]
[24]
He, Z.X.; Gong, Y.P.; Zhang, X.; Ma, L.Y.; Zhao, W. Pyridazine as a privileged structure: An updated review on anticancer activity of pyridazine containing bioactive molecules. Eur. J. Med. Chem., 2021, 209, 112946.
[http://dx.doi.org/10.1016/j.ejmech.2020.112946] [PMID: 33129590]
[25]
Patil, S.B. Biological and medicinal significance of pyrimidines: a review. Int. J. Pharm. Sci. Res., 2018, 9(1), 44-52.
[http://dx.doi.org/10.13040/ijpsr.0975-8232.9(1).44-52]
[26]
Chan, H.C.S.; Shan, H.; Dahoun, T.; Vogel, H.; Yuan, S. Advancing drug discovery via artificial intelligence. Trends Pharmacol. Sci., 2019, 40(8), 592-604.
[http://dx.doi.org/10.1016/j.tips.2019.06.004] [PMID: 31320117]
[27]
Simões, R.S.; Maltarollo, V.G.; Oliveira, P.R.; Honorio, K.M. Transfer and multi-task learning in QSAR modeling: advances and challenges. Front. Pharmacol., 2018, 9, 74.
[http://dx.doi.org/10.3389/fphar.2018.00074] [PMID: 29467659]
[28]
Danishuddin, ; Khan, A.U. Descriptors and their selection methods in QSAR analysis: paradigm for drug design. Drug Discov. Today, 2016, 21(8), 1291-1302.
[http://dx.doi.org/10.1016/j.drudis.2016.06.013] [PMID: 27326911]
[29]
Naqvi, A.A.T.; Mohammad, T.; Hasan, G.M.; Hassan, M.I. Advancements in docking and molecular dynamics simulations towards ligand-receptor interactions and structure - function relationships. Curr. Top. Med. Chem., 2018, 18(20), 1755-1768.
[http://dx.doi.org/10.2174/1568026618666181025114157] [PMID: 30360721]
[30]
Csermely, P.; Korcsmáros, T.; Kiss, H.J.M.; London, G.; Nussinov, R. Structure and dynamics of molecular networks: A novel paradigm of drug discovery. Pharmacol. Ther., 2013, 138(3), 333-408.
[http://dx.doi.org/10.1016/j.pharmthera.2013.01.016] [PMID: 23384594]
[31]
Jacunski, A.; Tatonetti, N.P. Connecting the dots: applications of network medicine in pharmacology and disease. Clin. Pharmacol. Ther., 2013, 94(6), 659-669.
[http://dx.doi.org/10.1038/clpt.2013.168] [PMID: 23995266]
[32]
Hopkins, A.L. Network pharmacology: the next paradigm in drug discovery. Nat. Chem. Biol., 2008, 4(11), 682-690.
[http://dx.doi.org/10.1038/nchembio.118] [PMID: 18936753]
[33]
Chen, J.; Teng, D.; Wu, Z.; Li, W.; Feng, Y.; Tang, Y.; Liu, G. Insights into the molecular mechanisms of liuwei dihuang decoction via network pharmacology. Chem. Res. Toxicol., 2021, 34(1), 91-102.
[http://dx.doi.org/10.1021/acs.chemrestox.0c00359] [PMID: 33332098]
[34]
Wang, W.; Wang, S.; Liu, T.; Ma, Y.; Huang, S.; Lei, L.; Wen, A.; Ding, Y. Resveratrol: multi-targets mechanism on neurodegenerative diseases based on network pharmacology. Front. Pharmacol., 2020, 11, 694.
[http://dx.doi.org/10.3389/fphar.2020.00694] [PMID: 32477148]
[35]
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.
[http://dx.doi.org/10.1016/j.drudis.2013.01.008] [PMID: 23340113]
[36]
Peters, J.U. Polypharmacology - foe or friend? J. Med. Chem., 2013, 56(22), 8955-8971.
[http://dx.doi.org/10.1021/jm400856t] [PMID: 23919353]
[37]
Mao, Q.; Dai, X.; Xu, G.; Su, Y.; Zhang, B.; Liu, D.; Wang, S. Design, synthesis and biological evaluation of 2-(4-alkoxy-3-cyano)phenyl-6-oxo-1,6-dihydropyrimidine-5-carboxylic acid derivatives as novel xanthine oxidase inhibitors. Eur. J. Med. Chem., 2019, 181, 111558.
[http://dx.doi.org/10.1016/j.ejmech.2019.07.061] [PMID: 31369933]
[38]
Shi, A.; Zhang, L.; Wang, H.; Wang, S.; Yang, M.; Guan, Q.; Bao, K.; Zhang, W. Design, synthesis and bioevaluation of 2-mercapto-6-phenylpyrimidine-4-carboxylic acid derivatives as potent xanthine oxidase inhibitors. Eur. J. Med. Chem., 2018, 155, 590-595.
[http://dx.doi.org/10.1016/j.ejmech.2018.06.009] [PMID: 29920453]
[39]
Zhang, B.; Dai, X.; Bao, Z.; Mao, Q.; Duan, Y.; Yang, Y.; Wang, S. Targeting the subpocket in xanthine oxidase: Design, synthesis, and biological evaluation of 2-[4-alkoxy-3-(1H-tetrazol-1-yl) phenyl]-6-oxo-1,6-dihydropyrimidine-5-carboxylic acid derivatives. Eur. J. Med. Chem., 2019, 181, 111559.
[http://dx.doi.org/10.1016/j.ejmech.2019.07.062] [PMID: 31376568]
[40]
Zhang, L.; Wang, S.; Yang, M.; Shi, A.; Wang, H.; Guan, Q.; Bao, K.; Zhang, W. Design, synthesis and bioevaluation of 3-oxo-6-aryl-2,3-dihydropyridazine-4-carbohydrazide derivatives as novel xanthine oxidase inhibitors. Bioorg. Med. Chem., 2019, 27(9), 1818-1823.
[http://dx.doi.org/10.1016/j.bmc.2019.03.027] [PMID: 30885567]
[41]
Gomes, R.A.; Genesi, G.L.; Maltarollo, V.G.; Trossini, G.H.G. Quantitative structure–activity relationships (HQSAR, CoMFA, and CoMSIA) studies for COX-2 selective inhibitors. J. Biomol. Struct. Dyn., 2017, 35(7), 1436-1445.
[http://dx.doi.org/10.1080/07391102.2016.1185379] [PMID: 27145042]
[42]
Yu, S.; Yuan, J.; Shi, J.; Ruan, X.; Zhang, T.; Wang, Y.; Du, Y. HQSAR and topomer CoMFA for predicting melanocortin-4 receptor binding affinities of trans-4-(4-chlorophenyl) pyrrolidine-3-carboxamides. Chemom. Intell. Lab. Syst., 2015, 146, 34-41.
[http://dx.doi.org/10.1016/j.chemolab.2015.04.017]
[43]
Klebe, G.; Abraham, U. Comparative molecular similarity index analysis (CoMSIA) to study hydrogen-bonding properties and to score combinatorial libraries. J. Comput. Aided Mol. Des., 1999, 13(1), 1-10.
[http://dx.doi.org/10.1023/A:1008047919606] [PMID: 10087495]
[44]
Klebe, G.; Abraham, U.; Mietzner, T. Molecular similarity indices in a comparative analysis (CoMSIA) of drug molecules to correlate and predict their biological activity. J. Med. Chem., 1994, 37(24), 4130-4146.
[http://dx.doi.org/10.1021/jm00050a010] [PMID: 7990113]
[45]
Bush, B.L.; Nachbar, R.B., Jr Sample-distance partial least squares: PLS optimized for many variables, with application to CoMFA. J. Comput. Aided Mol. Des., 1993, 7(5), 587-619.
[http://dx.doi.org/10.1007/BF00124364] [PMID: 8294948]
[46]
Jain, A.N. Surflex-Dock 2.1: Robust performance from ligand energetic modeling, ring flexibility, and knowledge-based search. J. Comput. Aided Mol. Des., 2007, 21(5), 281-306.
[http://dx.doi.org/10.1007/s10822-007-9114-2] [PMID: 17387436]
[47]
Eberhardt, J.; Santos-Martins, D.; Tillack, A.F.; Forli, S. Autodock vina 1.2.0: new docking methods, expanded force field, and python bindings. J. Chem. Inf. Model., 2021, 61(8), 3891-3898.
[http://dx.doi.org/10.1021/acs.jcim.1c00203] [PMID: 34278794]
[48]
Götz, A.W.; Williamson, M.J.; Xu, D.; Poole, D.; Le Grand, S.; Walker, R.C. Routine microsecond molecular dynamics simulations with AMBER on GPUs. 1. Generalized Born. J. Chem. Theory Comput., 2012, 8(5), 1542-1555.
[http://dx.doi.org/10.1021/ct200909j] [PMID: 22582031]
[49]
Salomon-Ferrer, R.; Götz, A.W.; Poole, D.; Le Grand, S.; Walker, R.C. Routine microsecond molecular dynamics simulations with AMBER on GPUs. 2. Explicit solvent particle mesh ewald. J. Chem. Theory Comput., 2013, 9(9), 3878-3888.
[http://dx.doi.org/10.1021/ct400314y] [PMID: 26592383]
[50]
Maier, J.A.; Martinez, C.; Kasavajhala, K.; Wickstrom, L.; Hauser, K.E.; Simmerling, C. ff14SB: Improving the accuracy of protein side chain and backbone parameters from ff99SB. J. Chem. Theory Comput., 2015, 11(8), 3696-3713.
[http://dx.doi.org/10.1021/acs.jctc.5b00255] [PMID: 26574453]
[51]
Lu, T.; Chen, F. Multiwfn: A multifunctional wavefunction analyzer. J. Comput. Chem., 2012, 33(5), 580-592.
[http://dx.doi.org/10.1002/jcc.22885] [PMID: 22162017]
[52]
Schauperl, M.; Nerenberg, P.S.; Jang, H.; Wang, L.P.; Bayly, C.I.; Mobley, D.L.; Gilson, M.K. Non-bonded force field model with advanced restrained electrostatic potential charges (RESP2). Commun. Chem., 2020, 3(1), 44.
[http://dx.doi.org/10.1038/s42004-020-0291-4] [PMID: 34136662]
[53]
Roe, D.R.; Cheatham, T.E., III 3rd. PTRAJ and CPPTRAJ: Software for processing and analysis of molecular dynamics trajectory data. J. Chem. Theory Comput., 2013, 9(7), 3084-3095.
[http://dx.doi.org/10.1021/ct400341p] [PMID: 26583988]
[54]
Miller, B.R., III; McGee, T.D., Jr; Swails, J.M.; Homeyer, N.; Gohlke, H.; Roitberg, A.E., 3rd.; McGee, T.D.Jr.; Swails, J.M.; Homeyer, N.; Gohlke, H.; Roitberg, A.E. MMPBSA.py: an efficient program for end-state free energy calculations. J. Chem. Theory Comput., 2012, 8(9), 3314-3321.
[http://dx.doi.org/10.1021/ct300418h] [PMID: 26605738]
[55]
Duvaud, S.; Gabella, C.; Lisacek, F.; Stockinger, H.; Ioannidis, V.; Durinx, C. Expasy, the Swiss Bioinformatics Resource Portal, as designed by its users. Nucleic Acids Res., 2021, 49(W1), W216-W227.
[http://dx.doi.org/10.1093/nar/gkab225] [PMID: 33849055]
[56]
Genheden, S.; Ryde, U. The MM/PBSA and MM/GBSA methods to estimate ligand-binding affinities. Expert Opin. Drug Discov., 2015, 10(5), 449-461.
[http://dx.doi.org/10.1517/17460441.2015.1032936] [PMID: 25835573]
[57]
Hou, T.; Wang, J.; Li, Y.; Wang, W. Assessing the performance of the MM/PBSA and MM/GBSA methods. 1. The accuracy of binding free energy calculations based on molecular dynamics simulations. J. Chem. Inf. Model., 2011, 51(1), 69-82.
[http://dx.doi.org/10.1021/ci100275a] [PMID: 21117705]
[58]
Shannon, P.; Markiel, A.; Ozier, O.; Baliga, N.S.; Wang, J.T.; Ramage, D.; Amin, N.; Schwikowski, B.; Ideker, T. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res., 2003, 13(11), 2498-2504.
[http://dx.doi.org/10.1101/gr.1239303] [PMID: 14597658]
[59]
Keiser, M.J.; Roth, B.L.; Armbruster, B.N.; Ernsberger, P.; Irwin, J.J.; Shoichet, B.K. Relating protein pharmacology by ligand chemistry. Nat. Biotechnol., 2007, 25(2), 197-206.
[http://dx.doi.org/10.1038/nbt1284] [PMID: 17287757]
[60]
Davis, A.P.; Wiegers, T.C.; Wiegers, J.; Grondin, C.J.; Johnson, R.J.; Sciaky, D.; Mattingly, C.J. CTD anatomy: Analyzing chemical-induced phenotypes and exposures from an anatomical perspective, with implications for environmental health studies. Curr. Res. Toxicol., 2021, 2, 128-139.
[http://dx.doi.org/10.1016/j.crtox.2021.03.001] [PMID: 33768211]
[61]
Kanehisa, M.; Furumichi, M.; Sato, Y.; Ishiguro-Watanabe, M.; Tanabe, M. KEGG: integrating viruses and cellular organisms. Nucleic Acids Res., 2021, 49(D1), D545-D551.
[http://dx.doi.org/10.1093/nar/gkaa970] [PMID: 33125081]
[62]
Szklarczyk, D.; Gable, A.L.; Lyon, D.; Junge, A.; Wyder, S.; Huerta-Cepas, J.; Simonovic, M.; Doncheva, N.T.; Morris, J.H.; Bork, P.; Jensen, L.J.; Mering, C. STRING v11: protein–protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets. Nucleic Acids Res., 2019, 47(D1), D607-D613.
[http://dx.doi.org/10.1093/nar/gky1131] [PMID: 30476243]
[63]
Sherman, B.T.; Hao, M.; Qiu, J.; Jiao, X.; Baseler, M.W.; Lane, H.C.; Imamichi, T.; Chang, W. DAVID: a web server for functional enrichment analysis and functional annotation of gene lists (2021 update). Nucleic Acids Res., 2022, 50(W1), W216-W221.
[http://dx.doi.org/10.1093/nar/gkac194] [PMID: 35325185]
[64]
Zhang, T.; Lv, Y.; Lei, Y.; Liu, D.; Feng, Y.; Zhao, J.; Chen, S.; Meng, F.; Wang, S. Design, synthesis and biological evaluation of 1-hydroxy-2-phenyl-4-pyridyl-1H-imidazole derivatives as xanthine oxidase inhibitors. Eur. J. Med. Chem., 2018, 146, 668-677.
[http://dx.doi.org/10.1016/j.ejmech.2018.01.060] [PMID: 29407989]
[65]
Xu, X.; Deng, L.; Nie, L.; Chen, Y.; Liu, Y.; Xie, R.; Li, Z. Discovery of 2-phenylthiazole-4-carboxylic acid, a novel and potent scaffold as xanthine oxidase inhibitors. Bioorg. Med. Chem. Lett., 2019, 29(4), 525-528.
[http://dx.doi.org/10.1016/j.bmcl.2019.01.005] [PMID: 30630716]
[66]
Xiong, G.; Wu, Z.; Yi, J.; Fu, L.; Yang, Z.; Hsieh, C.; Yin, M.; Zeng, X.; Wu, C.; Lu, A.; Chen, X.; Hou, T.; Cao, D. ADMETlab 2.0: an integrated online platform for accurate and comprehensive predictions of ADMET properties. Nucleic Acids Res., 2021, 49(W1), W5-W14.
[http://dx.doi.org/10.1093/nar/gkab255] [PMID: 33893803]
[67]
Daina, A.; Michielin, O.; Zoete, V. SwissADME: a free web tool to evaluate pharmacokinetics, drug-likeness and medicinal chemistry friendliness of small molecules. Sci. Rep., 2017, 7(1), 42717.
[http://dx.doi.org/10.1038/srep42717] [PMID: 28256516]
[68]
Lipinski, C.A.; Lombardo, F.; Dominy, B.W.; Feeney, P.J. Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings. Adv. Drug Deliv. Rev., 2012, 64, 4-17.
[http://dx.doi.org/10.1016/j.addr.2012.09.019] [PMID: 11259830]
[69]
Hughes, J.D.; Blagg, J.; Price, D.A.; Bailey, S.; DeCrescenzo, G.A.; Devraj, R.V.; Ellsworth, E.; Fobian, Y.M.; Gibbs, M.E.; Gilles, R.W.; Greene, N.; Huang, E.; Krieger-Burke, T.; Loesel, J.; Wager, T.; Whiteley, L.; Zhang, Y. Physiochemical drug properties associated with in vivo toxicological outcomes. Bioorg. Med. Chem. Lett., 2008, 18(17), 4872-4875.
[http://dx.doi.org/10.1016/j.bmcl.2008.07.071] [PMID: 18691886]
[70]
Gleeson, M.P. Generation of a set of simple, interpretable ADMET rules of thumb. J. Med. Chem., 2008, 51(4), 817-834.
[http://dx.doi.org/10.1021/jm701122q] [PMID: 18232648]
[71]
Johnson, T.W.; Dress, K.R.; Edwards, M. Using the Golden Triangle to optimize clearance and oral absorption. Bioorg. Med. Chem. Lett., 2009, 19(19), 5560-5564.
[http://dx.doi.org/10.1016/j.bmcl.2009.08.045] [PMID: 19720530]
[72]
Clark, R.D.; Abrahamian, E. Using a staged multi-objective optimization approach to find selective pharmacophore models. J. Comput. Aided Mol. Des., 2009, 23(11), 765-771.
[http://dx.doi.org/10.1007/s10822-008-9227-2] [PMID: 18663585]
[73]
Fox, P.C.; Wolohan, P.R.N.; Abrahamian, E.; Clark, R.D. Parameterization and conformational sampling effects in pharmacophore multiplet searching. J. Chem. Inf. Model., 2008, 48(12), 2326-2334.
[http://dx.doi.org/10.1021/ci800234q] [PMID: 19053520]
[74]
Richmond, N.J.; Abrams, C.A.; Wolohan, P.R.N.; Abrahamian, E.; Willett, P.; Clark, R.D. GALAHAD: 1. Pharmacophore identification by hypermolecular alignment of ligands in 3D. J. Comput. Aided Mol. Des., 2006, 20(9), 567-587.
[http://dx.doi.org/10.1007/s10822-006-9082-y] [PMID: 17051338]
[75]
Golbraikh, A.; Shen, M.; Xiao, Z.; Xiao, Y.D.; Lee, K.H.; Tropsha, A. Rational selection of training and test sets for the development of validated QSAR models. J. Comput. Aided Mol. Des., 2003, 17(2/4), 241-253.
[http://dx.doi.org/10.1023/A:1025386326946] [PMID: 13677490]
[76]
Todeschini, R.; Ballabio, D.; Grisoni, F. Beware of unreliable Q2! A comparative study of regression metrics for predictivity assessment of QSAR models. J. Chem. Inf. Model., 2016, 56(10), 1905-1913.
[http://dx.doi.org/10.1021/acs.jcim.6b00277] [PMID: 27633067]
[77]
Bowers, K.J.; Chow, E.; Xu, H.; Dror, R.O.; Eastwood, M.P.; Gregersen, B.A.; Klepeis, J.L.; Kolossváry, I.; Moraes, M.A.; Sacerdoti, F.D.; Salmon, J.K.; Shan, Y.; Shaw, D.E. Proceedings of the 2006 ACM/IEEE conference on Supercomputing (SC ’06), Tampa, Florida, November 11-17, 2006, p. 84.
[http://dx.doi.org/10.1145/1188455.1188544]

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