Skip to main content

Advertisement

SpringerLink
Log in

Classification models and SAR analysis on CysLT1 receptor antagonists using machine learning algorithms

  • Original Article
  • Published:
Molecular Diversity Aims and scope Submit manuscript

Abstract

Cysteinyl leukotrienes 1 (CysLT1) receptor is a promising drug target for rhinitis or other allergic diseases. In our study, we built classification models to predict bioactivities of CysLT1 receptor antagonists. We built a dataset with 503 CysLT1 receptor antagonists which were divided into two groups: highly active molecules (IC50 < 1000 nM) and weakly active molecules (IC50 ≥ 1000 nM). The molecules were characterized by several descriptors including CORINA descriptors, MACCS fingerprints, Morgan fingerprint and molecular SMILES. For CORINA descriptors and two types of fingerprints, we used the random forests (RF) and deep neural networks (DNN) to build models. For molecular SMILES, we used recurrent neural networks (RNN) with the self-attention to build models. The accuracies of test sets for all models reached 85%, and the accuracy of the best model (Model 2C) was 93%. In addition, we made structure–activity relationship (SAR) analyses on CysLT1 receptor antagonists, which were based on the output from the random forest models and RNN model. It was found that highly active antagonists usually contained the common substructures such as tetrazoles, indoles and quinolines. These substructures may improve the bioactivity of the CysLT1 receptor antagonists.

Graphic Abstract

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others

References

  1. Li H, Tian Y, Xie L, Liu X, Huang Z, Su W (2020) Mesenchymal stem cells in allergic diseases: current status. Allergol Int 69:35–45. https://doi.org/10.1016/j.alit.2019.08.001

    Article  CAS  PubMed  Google Scholar 

  2. Wesemann Duane R, Nagler Cathryn R (2016) The microbiome, timing, and barrier function in the context of allergic disease. Immunity 44:728–738. https://doi.org/10.1016/j.immuni.2016.02.002

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Prescott S, Allen KJ (2011) Food allergy: riding the second wave of the allergy epidemic. Pediatr Allergy Immunol 22:155–160. https://doi.org/10.1111/j.1399-3038.2011.01145.x

    Article  PubMed  Google Scholar 

  4. Haeggström JZ, Funk CD (2011) Lipoxygenase and leukotriene pathways: biochemistry, biology, and roles in disease. Chem Rev 111:5866–5898. https://doi.org/10.1021/cr200246d

    Article  CAS  PubMed  Google Scholar 

  5. Wishart DS, Feunang YD, Guo AC, Lo EJ, Marcu A, Grant JR, Sajed T, Johnson D, Li C, Sayeeda Z, Assempour N, Iynkkaran I, Liu Y, Maciejewski A, Gale N, Wilson A, Chin L, Cummings R, Le D, Pon A, Knox C, Wilson M (2017) Drugbank 5.0: a major update to the drugbank database for 2018. Nucleic Acids Res 46:D1074–D1082. https://doi.org/10.1093/nar/gkx1037

    Article  CAS  PubMed Central  Google Scholar 

  6. Durst GL (1996) Classical and three-dimensional QSAR in agrochemistry. In: Hansch C, Fujita T (eds) American chemical society, Washington DC, USA. US and Export US$88.95. Clothbound, p 354. Chem Intell Lab Syst 34(2):303–304. https://doi.org/10.1016/0169-7439(96)00053-6

  7. Myint KZ, Xie X-Q (2010) Recent advances in fragment-based QSAR and multi-dimensional QSAR methods. Int J Mol 11:3846–3866

    Article  CAS  Google Scholar 

  8. Davis AM (2017) 3.15–Quantitative structure–activity relationships. In: Chackalamannil S, Rotella D, Ward SE (eds) Comprehensive medicinal chemistry III. Elsevier, Oxford, pp 379–392. https://doi.org/10.1016/B978-0-12-409547-2.12348-0

  9. Bajusz D, Rácz A and Héberger K (2017) 3.14–chemical data formats, fingerprints, and other molecular descriptions for database analysis and searching. In: Chackalamannil S, Rotella D, Ward SE (eds) Comprehensive medicinal chemistry III. Elsevier, Oxford, pp 329–378. https://doi.org/10.1016/B978-0-12-409547-2.12345-5

  10. CORINA Symphony, version 1.0; Molecular Networks GmbH, Germany and Altamira, LLC: USA, 2018. https://www.mn-am.com/products/corinasymphony. Accessed Sep 2020

  11. Weininger D (1988) Smiles, a chemical language and information system. 1. Introduction to methodology and encoding rules. J Chem Inf Comput Sci 28:31–36. https://doi.org/10.1021/ci00057a005

    Article  CAS  Google Scholar 

  12. Hutter MC (2010) Molecular descriptors for chemoinformatics (2nd ed.). By roberto todeschini and viviana consonni. 5:306–307. https://doi.org/10.1002/cmdc.200900399

  13. Matta CF (2014) Modeling biophysical and biological properties from the characteristics of the molecular electron density, electron localization and delocalization matrices, and the electrostatic potential. J Comput Chem 35(16):1165–1198. https://doi.org/10.1002/jcc.23608

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Shahlaei M (2013) Descriptor selection methods in quantitative structure–activity relationship studies: a review study. Chem Rev 113:8093–8103. https://doi.org/10.1021/cr3004339

    Article  CAS  PubMed  Google Scholar 

  15. Toussi CA, Haddadnia J, Matta CF (2020) Drug design by machine-trained elastic networks: Predicting ser/thr-protein kinase inhibitors’ activities. Mol Diversity. https://doi.org/10.1007/s11030-020-10074-6

    Article  Google Scholar 

  16. Yoshida K, Sakurai A (2003) Machine learning. Encyclopedia of information systems. Elsevier, New York, pp 103–114

    Chapter  Google Scholar 

  17. Zoppis I, Mauri G, Dondi R (2019) Kernel methods: Support vector machines. Encyclopedia of bioinformatics and computational biology. Academic Press, Oxford, pp 503–510

    Chapter  Google Scholar 

  18. Fratello M, Tagliaferri R (2019) Decision trees and random forests. Encyclopedia of bioinformatics and computational biology. Academic Press, Oxford, pp 374–383

    Chapter  Google Scholar 

  19. Pearce J (2009) Regression, linear and nonlinear. International encyclopedia of human geography. Elsevier, Oxford, pp 302–308

    Chapter  Google Scholar 

  20. Schmidhuber J (2015) Deep learning in neural networks: an overview. Neural Netw 61:85–117. https://doi.org/10.1016/j.neunet.2014.09.003

    Article  PubMed  Google Scholar 

  21. Ghasemi F, Mehridehnavi A, Pérez-Garrido A, Pérez-Sánchez H (2018) Neural network and deep-learning algorithms used in qsar studies: merits and drawbacks. Drug Discov Today 23:1784–1790. https://doi.org/10.1016/j.drudis.2018.06.016

    Article  CAS  PubMed  Google Scholar 

  22. Chen H, Engkvist O, Wang Y, Olivecrona M, Blaschke T (2018) The rise of deep learning in drug discovery. Drug Discov Today 23:1241–1250. https://doi.org/10.1016/j.drudis.2018.01.039

    Article  PubMed  Google Scholar 

  23. Dong X, Wang L, Huang X, Liu T, Wei E, Du L, Yang B, Hu Y (2010) Pharmacophore identification, synthesis, and biological evaluation of carboxylated chalcone derivatives as cyslt1 antagonists. Bioorganic Med Chem 18:5519–5527. https://doi.org/10.1016/j.bmc.2010.06.047

    Article  CAS  Google Scholar 

  24. Palomer A, Pascual J, Cabré F, García ML, Mauleón D (2000) Derivation of pharmacophore and comfa models for leukotriene d4 receptor antagonists of the quinolinyl(bridged)aryl series. J Med Chem 43:392–400. https://doi.org/10.1021/jm990387k

    Article  CAS  PubMed  Google Scholar 

  25. Benedetti P, Mannhold R, Cruciani G, Ottaviani G (2004) Grind/almond investigations on cyslt1 receptor antagonists of the quinolinyl(bridged)aryl type. Bioorganic Med Chem 12:3607–3617. https://doi.org/10.1016/j.bmc.2004.04.018

    Article  CAS  Google Scholar 

  26. Hornik K, Stinchcombe M, White H (1989) Multilayer feedforward networks are universal approximators. Neural networks 2(5):359–366

    Article  Google Scholar 

  27. Graves, Alex, Mohamed, Abdel R, Geoffrey (2013) Speech recognition with deep recurrent neural networks. In: International conference on acoustics speech and signal processing ICASSP. IEEE, pp 6645–6649

  28. Luginina AG, Anastasiia M et al (2019) Structure-based mechanism of cysteinyl leukotriene receptor inhibition by antiasthmatic drugs. Sci Adv. https://doi.org/10.1126/sciadv.aax2518

    Article  PubMed  PubMed Central  Google Scholar 

  29. Davies M, Nowotka M, Papadatos G, Dedman N, Gaulton A, Atkinson F, Bellis L, Overington JP (2015) Chembl web services: Streamlining access to drug discovery data and utilities. Nucleic Acids Res 43:W612–W620

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Reaxys Database Available at: https://www.reaxys.com.Accessed. Accessed Sep 2020

  31. Gilson MK, Liu T, Baitaluk M, Nicola G, Hwang L, Chong J (2015) Bindingdb in 2015: a public database for medicinal chemistry, computational chemistry and systems pharmacology. Nucleic Acids Res 44:D1045–D1053. https://doi.org/10.1093/nar/gkv1072

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Uller L, Mathiesen JM, Alenmyr L, Korsgren M, Ulven T, Högberg T, Andersson G, Persson CGA, Kostenis E (2007) Antagonism of the prostaglandin d2 receptor crth2 attenuates asthma pathology in mouse eosinophilic airway inflammation. Respir Res 8:16. https://doi.org/10.1186/1465-9921-8-16

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Labelle M, Gareau Y, Dufresne C, Lau CK, Belley M, Jones TR, Leblanc Y, McAuliffe M, McFarlane CS, Metters KM, Ouimet N, Perrier H, Rochette C, Sawyer N, Slipetz D, Xiang YB, Wang Z, Pickett CB, Ford-Hutchinson AW, Young RN, Zamboni RJ (1995) Discovery of l-740,515, a potent thienopyridine cyslt1 receptor (ltd4 receptor) antagonist. Bioorganic Med Chem Lett 5:2551–2556. https://doi.org/10.1016/0960-894X(95)00448-3

    Article  CAS  Google Scholar 

  34. Frey EA, Nicholson DW, Metters KM (1993) Characterization of the leukotriene d4 receptor in dimethylsulphoxide-differentiated u937 cells: comparison with the leukotriene d4 receptor in human lung and guinea-pig lung. Eur J Pharmacol Mol Pharmacol 244:239–250. https://doi.org/10.1016/0922-4106(93)90149-4

    Article  CAS  Google Scholar 

  35. Gauthier JY, Jones T, Champion E, Charette L, Dehaven R, Ford-Hutchinson AW, Hoogsteen K, Lord A, Masson P (1990) Stereospecific synthesis, assignment of absolute configuration, and biological activity of the enantiomers of 3-[[[3-[2-(7-chloroquinolin-2-yl)-(e)-ethenyl]phenyl][[3-(dimethylamino)-3-oxopropyl]thio]methyl]thio]propionic acid, a potent and specific leukotriene d4 receptor antagonist. J Med Chem 33:2841–2845. https://doi.org/10.1021/jm00172a025

    Article  CAS  PubMed  Google Scholar 

  36. Connor DT, Cetenko WA, Mullican MD, Sorenson RJ, Unangst PC, Weikert RJ, Adolphson RL, Kennedy JA, Thueson DO (1992) Novel benzothiophene-, benzofuran-, and naphthalenecarboxamidotetrazoles as potential antiallergy agents. J Med Chem 35:958–965. https://doi.org/10.1021/jm00083a023

    Article  CAS  PubMed  Google Scholar 

  37. Itadani S, Yashiro K, Aratani Y, Sekiguchi T, Kinoshita A, Moriguchi H, Ohta N, Takahashi S, Ishida A, Tajima Y, Hisaichi K, Ima M, Ueda J, Egashira H, Sekioka T, Kadode M, Yonetomi Y, Nakao T, Inoue A, Nomura H, Kitamine T, Fujita M, Nabe T, Yamaura Y, Matsumura N, Imagawa A, Nakayama Y, Takeuchi J, Ohmoto K (2015) Discovery of gemilukast (ono-6950), a dual cyslt1 and cyslt2 antagonist as a therapeutic agent for asthma. J Med Chem 58:6093–6113. https://doi.org/10.1021/acs.jmedchem.5b00741

    Article  CAS  PubMed  Google Scholar 

  38. Mallinger A, Schiemann K, Rink C, Stieber F, Calderini M, Crumpler S, Stubbs M, Adeniji-Popoola O, Poeschke O, Busch M, Czodrowski P, Musil D, Schwarz D, Ortiz-Ruiz M-J, Schneider R, Thai C, Valenti M, de Haven BA, Burke R, Workman P, Dale T, Wienke D, Clarke PA, Esdar C, Raynaud FI, Eccles SA, Rohdich F, Blagg J (2016) Discovery of potent, selective, and orally bioavailable small-molecule modulators of the mediator complex-associated kinases cdk8 and cdk19. J Med Chem 59:1078–1101. https://doi.org/10.1021/acs.jmedchem.5b01685

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Eggert E, Hillig RC, Koehr S, Stöckigt D, Weiske J, Barak N, Mowat J, Brumby T, Christ CD, ter Laak A, Lang T, Fernandez-Montalvan AE, Badock V, Weinmann H, Hartung IV, Barsyte-Lovejoy D, Szewczyk M, Kennedy S, Li F, Vedadi M, Brown PJ, Santhakumar V, Arrowsmith CH, Stellfeld T, Stresemann C (2016) Discovery and characterization of a highly potent and selective aminopyrazoline-based in vivo probe (bay-598) for the protein lysine methyltransferase smyd2. J Med Chem 59:4578–4600. https://doi.org/10.1021/acs.jmedchem.5b01890

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Tran T-A, Kramer B, Shin Y-J, Vallar P, Boatman PD, Zou N, Sage CR, Gharbaoui T, Krishnan A, Pal B, Shakya SR, Garrido Montalban A, Adams JW, Ramirez J, Behan DP, Shifrina A, Blackburn A, Leakakos T, Shi Y, Morgan M, Sadeque A, Chen W, Unett DJ, Gaidarov I, Chen X, Chang S, Shu H-H, Tung S-F, Semple G (2017) Discovery of 2-(((1r,4r)-4-(((4-chlorophenyl)(phenyl)carbamoyl)oxy)methyl)cyclohexyl)methoxy)acetate (ralinepag): an orally active prostacyclin receptor agonist for the treatment of pulmonary arterial hypertension. J Med Chem 60:913–927. https://doi.org/10.1021/acs.jmedchem.6b00871

    Article  CAS  PubMed  Google Scholar 

  41. Cheeseman MD, Chessum NEA, Rye CS, Pasqua AE, Tucker MJ, Wilding B, Evans LE, Lepri S, Richards M, Sharp SY, Ali S, Rowlands M, O’Fee L, Miah A, Hayes A, Henley AT, Powers M, te Poele R, De Billy E, Pellegrino L, Raynaud F, Burke R, van Montfort RLM, Eccles SA, Workman P, Jones K (2017) Discovery of a chemical probe bisamide (cct251236): an orally bioavailable efficacious pirin ligand from a heat shock transcription factor 1 (hsf1) phenotypic screen. J Med Chem 60:180–201. https://doi.org/10.1021/acs.jmedchem.6b01055

    Article  CAS  PubMed  Google Scholar 

  42. Nance KD, Days EL, Weaver CD, Coldren A, Farmer TD, Cho HP, Niswender CM, Blobaum AL, Niswender KD, Lindsley CW (2017) Discovery of a novel series of orally bioavailable and cns penetrant glucagon-like peptide-1 receptor (glp-1r) noncompetitive antagonists based on a 1,3-disubstituted-7-aryl-5,5-bis(trifluoromethyl)-5,8-dihydropyrimido[4,5-d]pyrimidine-2,4(1h,3h)-dione core. J Med Chem 60:1611–1616. https://doi.org/10.1021/acs.jmedchem.6b01706

    Article  CAS  PubMed  Google Scholar 

  43. Felts AS, Rodriguez AL, Blobaum AL, Morrison RD, Bates BS, Thompson Gray A, Rook JM, Tantawy MN, Byers FW, Chang S, Venable DF, Luscombe VB, Tamagnan GD, Niswender CM, Daniels JS, Jones CK, Conn PJ, Lindsley CW, Emmitte KA (2017) Discovery of n-(5-fluoropyridin-2-yl)-6-methyl-4-(pyrimidin-5-yloxy)picolinamide (vu0424238): a novel negative allosteric modulator of metabotropic glutamate receptor subtype 5 selected for clinical evaluation. J Med Chem 60:5072–5085. https://doi.org/10.1021/acs.jmedchem.7b00410

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Beaufils F, Cmiljanovic N, Cmiljanovic V, Bohnacker T, Melone A, Marone R, Jackson E, Zhang X, Sele A, Borsari C, Mestan J, Hebeisen P, Hillmann P, Giese B, Zvelebil M, Fabbro D, Williams RL, Rageot D, Wymann MP (2017) 5-(4,6-dimorpholino-1,3,5-triazin-2-yl)-4-(trifluoromethyl)pyridin-2-amine (pqr309), a potent, brain-penetrant, orally bioavailable, pan-class i pi3k/mtor inhibitor as clinical candidate in oncology. J Med Chem 60:7524–7538. https://doi.org/10.1021/acs.jmedchem.7b00930

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Williamson DS, Smith GP, Acheson-Dossang P, Bedford ST, Chell V, Chen IJ, Daechsel JCA, Daniels Z, David L, Dokurno P, Hentzer M, Herzig MC, Hubbard RE, Moore JD, Murray JB, Newland S, Ray SC, Shaw T, Surgenor AE, Terry L, Thirstrup K, Wang Y, Christensen KV (2017) Design of leucine-rich repeat kinase 2 (lrrk2) inhibitors using a crystallographic surrogate derived from checkpoint kinase 1 (chk1). J Med Chem 60:8945–8962. https://doi.org/10.1021/acs.jmedchem.7b01186

    Article  CAS  PubMed  Google Scholar 

  46. Wu C-H, Song J-S, Kuan H-H, Wu S-H, Chou M-C, Jan J-J, Tsou LK, Ke Y-Y, Chen C-T, Yeh K-C, Wang S-Y, Yeh T-K, Tseng C-T, Huang C-L, Wu M-H, Kuo P-C, Lee C-J, Shia K-S (2018) Development of stem-cell-mobilizing agents targeting cxcr4 receptor for peripheral blood stem cell transplantation and beyond. J Med Chem 61:818–833. https://doi.org/10.1021/acs.jmedchem.7b01322

    Article  CAS  PubMed  Google Scholar 

  47. Come JH, Collier PN, Henderson JA, Pierce AC, Davies RJ, Le Tiran A, O’Dowd H, Bandarage UK, Cao J, Deininger D, Grey R, Krueger EB, Lowe DB, Liang J, Liao Y, Messersmith D, Nanthakumar S, Sizensky E, Wang J, Xu J, Chin EY, Damagnez V, Doran JD, Dworakowski W, Griffith JP, Jacobs MD, Khare-Pandit S, Mahajan S, Moody CS, Aronov AM (2018) Design and synthesis of a novel series of orally bioavailable, cns-penetrant, isoform selective phosphoinositide 3-kinase γ (pi3kγ) inhibitors with potential for the treatment of multiple sclerosis (ms). J Med Chem 61:5245–5256. https://doi.org/10.1021/acs.jmedchem.8b00085

    Article  CAS  PubMed  Google Scholar 

  48. Humphrey JM, Movsesian M, Ende CW, Becker SL, Chappie TA, Jenkinson S, Liras JL, Liras S, Orozco C, Pandit J, Vajdos FF, Vandeput F, Yang E, Menniti FS (2018) Discovery of potent and selective periphery-restricted quinazoline inhibitors of the cyclic nucleotide phosphodiesterase pde1. J Med Chem 61:4635–4640. https://doi.org/10.1021/acs.jmedchem.8b00374

    Article  CAS  PubMed  Google Scholar 

  49. Schierle S, Flauaus C, Heitel P, Willems S, Schmidt J, Kaiser A, Weizel L, Goebel T, Kahnt AS, Geisslinger G, Steinhilber D, Wurglics M, Rovati GE, Schmidtko A, Proschak E, Merk D (2018) Boosting anti-inflammatory potency of zafirlukast by designed polypharmacology. J Med Chem 61:5758–5764. https://doi.org/10.1021/acs.jmedchem.8b00458

    Article  CAS  PubMed  Google Scholar 

  50. Reed CW, Yohn SE, Washecheck JP, Roenfanz HF, Quitalig MC, Luscombe VB, Jenkins MT, Rodriguez AL, Engers DW, Blobaum AL, Conn PJ, Niswender CM, Lindsley CW (2019) Discovery of an orally bioavailable and central nervous system (cns) penetrant mglu7 negative allosteric modulator (nam) in vivo tool compound: N-(2-(1h–1,2,4-triazol-1-yl)-5-(trifluoromethoxy)phenyl)-4-(cyclopropylmethoxy)-3-methoxybenzamide (vu6012962). J Med Chem 62:1690–1695. https://doi.org/10.1021/acs.jmedchem.8b01810

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Xu R, Wang K, Rizzi JP, Huang H, Grina JA, Schlachter ST, Wang B, Wehn PM, Yang H, Dixon DD, Czerwinski RM, Du X, Ged EL, Han G, Tan H, Wong T, Xie S, Josey JA, Wallace EM (2019) 3-[(1s,2s,3r)-2,3-difluoro-1-hydroxy-7-methylsulfonylindan-4-yl]oxy-5-fluorobenzonitrile (pt2977), a hypoxia-inducible factor 2α (hif-2α) inhibitor for the treatment of clear cell renal cell carcinoma. J Med Chem 62:6876–6893. https://doi.org/10.1021/acs.jmedchem.9b00719

    Article  CAS  PubMed  Google Scholar 

  52. Schnider P, Bissantz C, Bruns A, Dolente C, Goetschi E, Jakob-Roetne R, Künnecke B, Mueggler T, Muster W, Parrott N, Pinard E, Ratni H, Risterucci C, Rogers-Evans M, von Kienlin M, Grundschober C (2020) Discovery of balovaptan, a vasopressin 1a receptor antagonist for the treatment of autism spectrum disorder. J Med Chem 63:1511–1525. https://doi.org/10.1021/acs.jmedchem.9b01478

    Article  CAS  PubMed  Google Scholar 

  53. Jeffries DE, Lindsley CW (2017) Total synthesis and biological evaluation of hybrubin a. J Org Chem 82:431–437. https://doi.org/10.1021/acs.joc.6b02534

    Article  CAS  PubMed  Google Scholar 

  54. Hu C, Yang J, He Q, Luo Y, Chen Z, Yang L, Yi H, Li H, Xia H, Ran D, Yang Y, Zhang J, Li Y, Wang H (2018) Cysltr1 blockage ameliorates liver injury caused by aluminum-overload via pi3k/akt/mtor-mediated autophagy activation in vivo and in vitro. Mol Pharm 15:1996–2006. https://doi.org/10.1021/acs.molpharmaceut.8b00121

    Article  CAS  PubMed  Google Scholar 

  55. Rook JM, Abe M, Cho HP, Nance KD, Luscombe VB, Adams JJ, Dickerson JW, Remke DH, Garcia-Barrantes PM, Engers DW, Engers JL, Chang S, Foster JJ, Blobaum AL, Niswender CM, Jones CK, Conn PJ, Lindsley CW (2017) Diverse effects on m1 signaling and adverse effect liability within a series of m1 ago-pams. ACS Chem Neurosci 8:866–883. https://doi.org/10.1021/acschemneuro.6b00429

    Article  CAS  PubMed  Google Scholar 

  56. Bollinger KA, Felts AS, Brassard CJ, Engers JL, Rodriguez AL, Weiner RL, Cho HP, Chang S, Bubser M, Jones CK, Blobaum AL, Niswender CM, Conn PJ, Emmitte KA, Lindsley CW (2017) Design and synthesis of mglu2 nams with improved potency and cns penetration based on a truncated picolinamide core. ACS Med Chem Lett 8:919–924. https://doi.org/10.1021/acsmedchemlett.7b00279

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  57. Abe M, Seto M, Gogliotti RG, Loch MT, Bollinger KA, Chang S, Engelberg EM, Luscombe VB, Harp JM, Bubser M, Engers DW, Jones CK, Rodriguez AL, Blobaum AL, Conn PJ, Niswender CM, Lindsley CW (2017) Discovery of vu6005649, a cns penetrant mglu7/8 receptor pam derived from a series of pyrazolo[1,5-a]pyrimidines. ACS Med Chem Lett 8:1110–1115. https://doi.org/10.1021/acsmedchemlett.7b00317

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  58. Han S, Thoresen L, Jung J-K, Zhu X, Thatte J, Solomon M, Gaidarov I, Unett DJ, Yoon WH, Barden J, Sadeque A, Usmani A, Chen C, Semple G, Grottick AJ, Al-Shamma H, Christopher R, Jones RM (2017) Discovery of apd371: Identification of a highly potent and selective cb2 agonist for the treatment of chronic pain. ACS Med Chem Lett 8:1309–1313. https://doi.org/10.1021/acsmedchemlett.7b00396

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  59. Lewis TA, de Waal L, Wu X, Youngsaye W, Wengner A, Kopitz C, Lange M, Gradl S, Ellermann M, Lienau P, Schreiber SL, Greulich H, Meyerson M (2019) Optimization of pde3a modulators for slfn12-dependent cancer cell killing. ACS Med Chem Lett 10:1537–1542. https://doi.org/10.1021/acsmedchemlett.9b00360

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  60. Yu M, Ledeboer MW, Daniels M, Malojcic G, Tibbitts TT, Coeffet-Le Gal M, Pan-Zhou X-R, Westerling-Bui A, Beconi M, Reilly JF, Mundel P, Harmange J-C (2019) Discovery of a potent and selective trpc5 inhibitor, efficacious in a focal segmental glomerulosclerosis model. ACS Med Chem Lett 10:1579–1585. https://doi.org/10.1021/acsmedchemlett.9b00430

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  61. Yin Z, Chen Y-L, Schul W, Wang Q-Y, Gu F, Duraiswamy J, Kondreddi RR, Niyomrattanakit P, Lakshminarayana SB, Goh A, Xu HY, Liu W, Liu B, Lim JYH, Ng CY, Qing M, Lim CC, Yip A, Wang G, Chan WL, Tan HP, Lin K, Zhang B, Zou G, Bernard KA, Garrett C, Beltz K, Dong M, Weaver M, He H, Pichota A, Dartois V, Keller TH, Shi P-Y (2009) An adenosine nucleoside inhibitor of dengue virus. Proc Natl Acad Sci 106:20435. https://doi.org/10.1073/pnas.0907010106

    Article  PubMed  PubMed Central  Google Scholar 

  62. Lehto SG, Tamir R, Deng H, Klionsky L, Kuang R, Le A, Lee D, Louis J-C, Magal E, Manning BH, Rubino J, Surapaneni S, Tamayo N, Wang T, Wang J, Wang J, Wang W, Youngblood B, Zhang M, Zhu D, Norman MH, Gavva NR (2008) Antihyperalgesic effects of (R, E)-N-(2-hydroxy-2,3-dihydro-1H-inden-4-yl)-3-(2-(piperidin-1-yl)-4-(trifluoromethyl)phenyl)-acrylamide (amg8562), a novel transient receptor potential vanilloid type 1 modulator that does not cause hyperthermia in rats. J Pharmacol Exp Ther 326:218. https://doi.org/10.1124/jpet.107.132233

    Article  CAS  PubMed  Google Scholar 

  63. Brady AE, Jones CK, Bridges TM, Kennedy JP, Thompson AD, Heiman JU, Breininger ML, Gentry PR, Yin H, Jadhav SB, Shirey JK, Conn PJ, Lindsley CW (2008) Centrally active allosteric potentiators of the M4 muscarinic acetylcholine receptor reverse amphetamine-induced hyperlocomotor activity in rats. J Pharmacol Exp Ther 327:941. https://doi.org/10.1124/jpet.108.140350

    Article  CAS  PubMed  Google Scholar 

  64. Roberts A, Grafton G, Powell AD, Brock K, Chen C, Xie D, Huang J, Liu S, Cooper AJ, Brady CA, Qureshi O, Stamataki Z, Manning DD, Moore NA, Sargent BJ, Guzzo PR, Barnes NM (2020) Csti-300 (smp-100); a novel 5-HT3 receptor partial agonist with potential to treat patients with irritable bowel syndrome or carcinoid syndrome. J Pharmacol Exp Ther 373:122. https://doi.org/10.1124/jpet.119.261008

    Article  CAS  PubMed  Google Scholar 

  65. Schmitz A, Sankaranarayanan A, Azam P, Schmidt-Lassen K, Homerick D, Hänsel W, Wulff H (2005) Design of pap-1, a selective small molecule kv1.3 blocker, for the suppression of effector memory t cells in autoimmune diseases. Mol Pharmacol 68:1254. https://doi.org/10.1124/mol.105.015669

    Article  CAS  PubMed  Google Scholar 

  66. Niswender CM, Johnson KA, Weaver CD, Jones CK, Xiang Z, Luo Q, Rodriguez AL, Marlo JE, de Paulis T, Thompson AD, Days EL, Nalywajko T, Austin CA, Williams MB, Ayala JE, Williams R, Lindsley CW, Conn PJ (2008) Discovery, characterization, and antiparkinsonian effect of novel positive allosteric modulators of metabotropic glutamate receptor 4. Mol Pharmacol 74:1345. https://doi.org/10.1124/mol.108.049551

    Article  CAS  PubMed  Google Scholar 

  67. Sasso O, Pontis S, Armirotti A, Cardinali G, Kovacs D, Migliore M, Summa M, Moreno-Sanz G, Picardo M, Piomelli D (2016) Endogenous N-acyl taurines regulate skin wound healing. Proc Natl Acad Sci 113:E4397. https://doi.org/10.1073/pnas.1605578113

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  68. Gilson PR, Nguyen W, Poole WA, Teixeira JE, Thompson JK, Guo K, Stewart RJ, Ashton TD, White KL, Sanz LM, Gamo F-J, Charman SA, Wittlin S, Duffy J, Tonkin CJ, Tham W-H, Crabb BS, Cooke BM, Huston CD, Cowman AF, Sleebs BE (2019) Evaluation of 4-amino 2-anilinoquinazolines against plasmodium and other apicomplexan parasites in vitro and in a p. Falciparum humanized nod-scid il2rγ null mouse model of malaria. Antimicrob Agents Chemother 63:e01804-e1818. https://doi.org/10.1128/AAC.01804-18

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  69. Haskell CA, Horuk R, Liang M, Rosser M, Dunning L, Islam I, Kremer L, Gutiérrez J, Marquez G, Martinez-A C, Biscone MJ, Doms RW, Ribeiro S (2006) Identification and characterization of a potent, selective nonpeptide agonist of the cc chemokine receptor ccr8. Mol Pharmacol 69:309. https://doi.org/10.1124/mol.105.014779

    Article  CAS  PubMed  Google Scholar 

  70. Dupré DJ, Le Gouill C, Gingras D, Rola-Pleszczynski M, Staňková J (2004) Inverse agonist activity of selected ligands of the cysteinyl-leukotriene receptor 1. J Pharmacol Exp Ther 309:102. https://doi.org/10.1124/jpet.103.059824

    Article  CAS  PubMed  Google Scholar 

  71. Gleason JG, Hall RF, Perchonock CD, Erhard KF, Frazee JS, Ku TW, Kondrad K, McCarthy ME, Mong S (1987) High-affinity leukotriene receptor antagonists. Synthesis and pharmacological characterization of 2-hydroxy-3-[(2-carboxyethyl)thio]-3-[2-(8-phenyloctyl)phenyl]propanoic acid. J Med Chem 30:959–961. https://doi.org/10.1021/jm00389a001

    Article  CAS  PubMed  Google Scholar 

  72. Ulven T, Receveur J-M, Grimstrup M, Rist Ø, Frimurer TM, Gerlach L-O, Mathiesen JM, Kostenis E, Uller L, Högberg T (2006) Novel selective orally active crth2 antagonists for allergic inflammation developed from in silico derived hits. J Med Chem 49:6638–6641. https://doi.org/10.1021/jm060657g

    Article  CAS  PubMed  Google Scholar 

  73. Xia G, Benmohamed R, Kim J, Arvanites AC, Morimoto RI, Ferrante RJ, Kirsch DR, Silverman RB (2011) Pyrimidine-2,4,6-trione derivatives and their inhibition of mutant sod1-dependent protein aggregation. Toward a treatment for amyotrophic lateral sclerosis. J Med Chem 54:2409–2421. https://doi.org/10.1021/jm101549k

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  74. Wager TT, Pettersen BA, Schmidt AW, Spracklin DK, Mente S, Butler TW, Howard H, Lettiere DJ, Rubitski DM, Wong DF, Nedza FM, Nelson FR, Rollema H, Raggon JW, Aubrecht J, Freeman JK, Marcek JM, Cianfrogna J, Cook KW, James LC, Chatman LA, Iredale PA, Banker MJ, Homiski ML, Munzner JB, Chandrasekaran RY (2011) Discovery of two clinical histamine h3 receptor antagonists: Trans-n-ethyl-3-fluoro-3-[3-fluoro-4-(pyrrolidinylmethyl)phenyl]cyclobutanecarboxamide (pf-03654746) and trans-3-fluoro-3-[3-fluoro-4-(pyrrolidin-1-ylmethyl)phenyl]-n-(2-methylpropyl)cyclobutanecarboxamide (pf-03654764). J Med Chem 54:7602–7620. https://doi.org/10.1021/jm200939b

    Article  CAS  PubMed  Google Scholar 

  75. Brodney MA, Johnson DE, Sawant-Basak A, Coffman KJ, Drummond EM, Hudson EL, Fisher KE, Noguchi H, Waizumi N, McDowell LL, Papanikolaou A, Pettersen BA, Schmidt AW, Tseng E, Stutzman-Engwall K, Rubitski DM, Vanase-Frawley MA, Grimwood S (2012) Identification of multiple 5-ht4 partial agonist clinical candidates for the treatment of alzheimer’s disease. J Med Chem 55:9240–9254. https://doi.org/10.1021/jm300953p

    Article  CAS  PubMed  Google Scholar 

  76. Gentry PR, Kokubo M, Bridges TM, Noetzel MJ, Cho HP, Lamsal A, Smith E, Chase P, Hodder PS, Niswender CM, Daniels JS, Conn PJ, Lindsley CW, Wood MR (2014) Development of a highly potent, novel m5 positive allosteric modulator (pam) demonstrating cns exposure: 1-((1h-indazol-5-yl)sulfoneyl)-n-ethyl-n-(2-(trifluoromethyl)benzyl)piperidine-4-carboxamide (ml380). J Med Chem 57:7804–7810. https://doi.org/10.1021/jm500995y

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  77. Schroeder CE, Yao T, Sotsky J, Smith RA, Roy S, Chu Y-K, Guo H, Tower NA, Noah JW, McKellip S, Sosa M, Rasmussen L, Smith LH, White EL, Aubé J, Jonsson CB, Chung D, Golden JE (2014) Development of (e)-2-((1,4-dimethylpiperazin-2-ylidene)amino)-5-nitro-n-phenylbenzamide, ml336: Novel 2-amidinophenylbenzamides as potent inhibitors of venezuelan equine encephalitis virus. J Med Chem 57:8608–8621. https://doi.org/10.1021/jm501203v

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  78. Ratni H, Rogers-Evans M, Bissantz C, Grundschober C, Moreau J-L, Schuler F, Fischer H, Alvarez Sanchez R, Schnider P (2015) Discovery of highly selective brain-penetrant vasopressin 1a antagonists for the potential treatment of autism via a chemogenomic and scaffold hopping approach. J Med Chem 58:2275–2289. https://doi.org/10.1021/jm501745f

    Article  CAS  PubMed  Google Scholar 

  79. Sams AG, Mikkelsen GK, Larsen M, Langgård M, Howells ME, Schrøder TJ, Brennum LT, Torup L, Jørgensen EB, Bundgaard C, Kreilgård M, Bang-Andersen B (2011) Discovery of phosphoric acid mono-{2-[(e/z)-4-(3,3-dimethyl-butyrylamino)-3,5-difluoro-benzoylimino]-thiazol-3-ylmethyl} ester (lu aa47070): a phosphonooxymethylene prodrug of a potent and selective ha2a receptor antagonist. J Med Chem 54:751–764. https://doi.org/10.1021/jm1008659

    Article  CAS  PubMed  Google Scholar 

  80. Chen T, Benmohamed R, Kim J, Smith K, Amante D, Morimoto RI, Kirsch DR, Ferrante RJ, Silverman RB (2012) Adme-guided design and synthesis of aryloxanyl pyrazolone derivatives to block mutant superoxide dismutase 1 (sod1) cytotoxicity and protein aggregation: potential application for the treatment of amyotrophic lateral sclerosis. J Med Chem 55:515–527. https://doi.org/10.1021/jm2014277

    Article  CAS  PubMed  Google Scholar 

  81. Kolasa T, Gunn DE, Bhatia P, Woods KW, Gane T, Stewart AO, Bouska JB, Harris RR, Hulkower KI, Malo PE, Bell RL, Carter GW, Brooks CDW (2000) Heteroarylmethoxyphenylalkoxyiminoalkylcarboxylic acids as leukotriene biosynthesis inhibitors. J Med Chem 43:690–705. https://doi.org/10.1021/jm9904102

    Article  CAS  PubMed  Google Scholar 

  82. Berry CB, Bubser M, Jones CK, Hayes JP, Wepy JA, Locuson CW, Daniels JS, Lindsley CW, Hopkins CR (2014) Discovery and characterization of ml398, a potent and selective antagonist of the d4 receptor with in vivo activity. ACS Med Chem Lett 5:1060–1064. https://doi.org/10.1021/ml500267c

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  83. Chiang N, Riley IR, Dalli J, Rodriguez AR, Spur BW, Serhan CN (2018) New maresin conjugates in tissue regeneration pathway counters leukotriene d4–stimulated vascular responses. FASEB J 32:4043–4052. https://doi.org/10.1096/fj.201701493R

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  84. Wen W, Wang Y, Li Z, Tseng P-Y, McManus OB, Wu M, Li M, Lindsley CW, Dong X, Hopkins CR (2015) Discovery and characterization of 2-(cyclopropanesulfonamido)-n-(2-ethoxyphenyl)benzamide, ml382: A potent and selective positive allosteric modulator of mrgx1. Chem Med Chem 10:57–61. https://doi.org/10.1002/cmdc.201402277

    Article  CAS  PubMed  Google Scholar 

  85. Flaherty DP, Harris MT, Schroeder CE, Khan H, Kahney EW, Hackler AL, Patrick SL, Weiner WS, Aubé J, Sharlow ER, Morris JC, Golden JE (2017) Optimization and evaluation of antiparasitic benzamidobenzoic acids as inhibitors of kinetoplastid hexokinase 1. Chem Med Chem 12:1994–2005. https://doi.org/10.1002/cmdc.201700592

    Article  CAS  PubMed  Google Scholar 

  86. Schierle S, Helmstädter M, Schmidt J, Hartmann M, Horz M, Kaiser A, Weizel L, Heitel P, Proschak A, Hernandez-Olmos V, Proschak E, Merk D (2020) Dual farnesoid x receptor/soluble epoxide hydrolase modulators derived from zafirlukast. Chem Med Chem 15:50–67. https://doi.org/10.1002/cmdc.201900576

    Article  CAS  PubMed  Google Scholar 

  87. Capra V, Bolla M, Angelo Belloni P, Mezzetti M, Carlo Folco G, Nicosia S, Enrico Rovati G (1998) Pharmacological characterization of the cysteinyl-leukotriene antagonists cgp 45715a (iralukast) and cgp 57698 in human airways in vitro. Br J Pharmacol 123:590–598. https://doi.org/10.1038/sj.bjp.0701636

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  88. Sallinen J, Höglund I, Engström M, Lehtimäki J, Virtanen R, Sirviö J, Wurster S, Savola JM, Haapalinna A (2007) Pharmacological characterization and cns effects of a novel highly selective α2c-adrenoceptor antagonist jp-1302. Br J Pharmacol 150:391–402. https://doi.org/10.1038/sj.bjp.0707005

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  89. Oh KS, Lee JH, Yi KY, Lim CJ, Lee S, Park CH, Seo HW, Lee BH (2015) The orally active urotensin receptor antagonist, kr36676, attenuates cellular and cardiac hypertrophy. Br J Pharmacol 172:2618–2633. https://doi.org/10.1111/bph.13082

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  90. Salvatore CA, Hershey JC, Corcoran HA, Fay JF, Johnston VK, Moore EL, Mosser SD, Burgey CS, Paone DV, Shaw AW, Graham SL, Vacca JP, Williams TM, Koblan KS, Kane SA (2008) Pharmacological characterization of mk-0974 [N-[(3R,6S)-6-(2,3-difluorophenyl)-2-oxo-1-(2,2,2-trifluoroethyl)azepan-3-yl]-4-(2-oxo-2,3-dihydro-1H-imidazo[4,5-b]pyridin-1-yl)piperidine-1-carboxamide], a potent and orally active calcitonin gene-related peptide receptor antagonist for the treatment of migraine. J Pharmacol Exp Ther 324:416. https://doi.org/10.1124/jpet.107.130344

    Article  CAS  PubMed  Google Scholar 

  91. Abram TS, Böshagen H, Butler JE, Cuthbert NJ, Francis HP, Gardiner PJ, Hartwig W, Kluender HC, Norman P, Meier H, Rosentreter U, Schlemmer KH, Tudhope SR, Taylor WA (1993) A new structural analogue antagonist of peptido-leukotrienes. The discovery of bay x7195. Bioorganic Med Chem Lett 3:1517–1522. https://doi.org/10.1016/S0960-894X(00)80009-8

    Article  CAS  Google Scholar 

  92. Blanco M-J, Benesh DR, Knobelsdorf JA, Khilevich A, Cortez GS, Mokube F, Aicher TD, Groendyke TM, Marmsater FP, Tang TP, Johnson KW, Clemens-Smith A, Muhlhauser MA, Swanson S, Catlow J, Emkey R, Johnson MP, Schkeryantz JM (2017) Discovery of dual positive allosteric modulators (pams) of the metabotropic glutamate 2 receptor and cyslt1 antagonists for treating migraine headache. Bioorganic Med Chem Lett 27:323–328. https://doi.org/10.1016/j.bmcl.2016.11.049

    Article  CAS  Google Scholar 

  93. Iwaki Y, Yashiro K, Kokubo M, Mori T, Wieting JM, McGowan KM, Bridges TM, Engers DW, Denton JS, Kurata H, Lindsley CW (2019) Towards a trek-1/2 (twik-related k+ channel 1 and 2) dual activator tool compound: multi-dimensional optimization of bl-1249. Bioorganic Med Chem Lett 29:1601–1604. https://doi.org/10.1016/j.bmcl.2019.04.048

    Article  CAS  Google Scholar 

  94. Reiter LA, Melvin LS, Crean GL, Showell HJ, Koch K, Biggers MS, Cheng JB, Breslow R, Conklyn MJ, Farrell CA, Hada WA, Laird ER, Martin JJ, Todd Miller G, Pillar JS (1997) 3-substituted-4-hydroxy-7-chromanylacetic acid derivatives as antagonists of the leukotriene b4 (ltb4) receptor. Bioorganic Med Chem Lett 7:2307–2312. https://doi.org/10.1016/S0960-894X(97)00414-9

    Article  CAS  Google Scholar 

  95. Brown MF, Marfat A, Antognoli G, Chambers RJ, Cheng JB, Damon DB, Liston TE, McGlynn MA, O’Sullivan SP, Owens BS, Pillar JS, Shirley JT, Watson JW (1998) N-carbamoyl analogs of zafirlukast: Potent receptor antagonists of leukotriene d4. Bioorganic Med Chem Lett 8:2451–2456. https://doi.org/10.1016/S0960-894X(98)00442-9

    Article  CAS  Google Scholar 

  96. Chambers RJ, Antognoli GW, Cheng JB, Kuperman AV, Liston TC, Marfat A, Owens BS, Pillar JS, Shirley JT, Watson JW (1998) Development of 2,2-dimethylchromanol cysteinyl lt1 receptor antagonists. Bioorganic Med Chem Lett 8:3577–3582. https://doi.org/10.1016/S0960-894X(98)00654-4

    Article  CAS  Google Scholar 

  97. Cabré F, Carabaza A, Garcia AM, Calvo L, Cucchi P, Palomer A, Pascual J, Garcia ML, Manzini S, Lecci A, Crea A, Maggi CA (2002) Pharmacological profile of men91507, a new cyslt1 receptor antagonist. Eur J Pharmacol 451:317–326. https://doi.org/10.1016/S0014-2999(02)02232-X

    Article  PubMed  Google Scholar 

  98. Ravasi S, Capra V, Panigalli T, Rovati GE, Nicosia S (2002) Pharmacological differences among cyslt1 receptor antagonists with respect to ltc4 and ltd4 in human lung parenchyma. Biochem Pharmacol 63:1537–1546. https://doi.org/10.1016/S0006-2952(02)00889-4

    Article  CAS  PubMed  Google Scholar 

  99. Mamedova L, Capra V, Accomazzo MR, Gao Z-G, Ferrario S, Fumagalli M, Abbracchio MP, Rovati GE, Jacobson KA (2005) Cyslt1 leukotriene receptor antagonists inhibit the effects of nucleotides acting at p2y receptors. Biochem Pharmacol 71:115–125. https://doi.org/10.1016/j.bcp.2005.10.003

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  100. Suzuki K, Morokata T, Morihira K, Sato I, Takizawa S, Kaneko M, Takahashi K, Shimizu Y (2007) A dual antagonist for chemokine ccr3 receptor and histamine h1 receptor. Eur J Pharmacol 563:224–232. https://doi.org/10.1016/j.ejphar.2007.01.074

    Article  CAS  PubMed  Google Scholar 

  101. Dong X, Zhao Y, Huang X, Lin K, Chen J, Wei E, Liu T, Hu Y (2013) Structure-based drug design using gpcr homology modeling: toward the discovery of novel selective cyslt2 antagonists. Eur J Med Chem 62:754–763. https://doi.org/10.1016/j.ejmech.2013.01.041

    Article  CAS  PubMed  Google Scholar 

  102. Murase A, Taniguchi Y, Tonai-Kachi H, Nakao K, Takada J (2008) In vitro pharmacological characterization of cj-042794, a novel, potent, and selective prostaglandin ep4 receptor antagonist. Life Sci 82:226–232. https://doi.org/10.1016/j.lfs.2007.11.002

    Article  CAS  PubMed  Google Scholar 

  103. Hayashi S, Nakata E, Morita A, Mizuno K, Yamamura K, Kato A, Ohashi K (2010) Discovery of {1-[4-(2-{hexahydropyrrolo[3,4-c]pyrrol-2(1h)-yl}-1h-benzimidazol-1-yl)piperidin-1-yl]cyclooctyl}methanol, systemically potent novel non-peptide agonist of nociceptin/orphanin fq receptor as analgesic for the treatment of neuropathic pain: Design, synthesis, and structure–activity relationships. Bioorganic Med Chem 18:7675–7699. https://doi.org/10.1016/j.bmc.2010.07.034

    Article  CAS  Google Scholar 

  104. Itadani S, Takahashi S, Ima M, Sekiguchi T, Aratani Y, Egashira H, Matsumura N, Inoue A, Yonetomi Y, Fujita M, Nakayama Y, Takeuchi J (2015) Discovery of a potent, orally available dual cyslt1 and cyslt2 antagonist with dicarboxylic acid. Bioorganic Med Chem 23:2079–2097. https://doi.org/10.1016/j.bmc.2015.03.011

    Article  CAS  Google Scholar 

  105. Takahashi N, Take Y (2018) Tegoprazan, a novel potassium-competitive acid blocker to control gastric acid secretion and motility. J Pharmacol Exp Ther 364:275. https://doi.org/10.1124/jpet.117.244202

    Article  CAS  PubMed  Google Scholar 

  106. Panettieri RA, Tan EML, Ciocca V, Luttmann MA, Leonard TB, Hay DWP (1998) Effects of ltd4 on human airway smooth muscle cell proliferation, matrix expression, and contraction in vitro: differential sensitivity to cysteinyl leukotriene receptor antagonists. Am J Respir Cell Mol Biol 19:453–461. https://doi.org/10.1165/ajrcmb.19.3.2999

    Article  CAS  PubMed  Google Scholar 

  107. Sliwoski G, Kothiwale S, Meiler J, Lowe EW (2014) Computational methods in drug discovery. Pharmacol Rev 66:334. https://doi.org/10.1124/pr.112.007336

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  108. Molecular Operating Environment (MOE), 2015.10; Chemical Computing Group Inc., 1010 Sherbooke St. West, Suite #910,Montreal, QC, Canada, H3A 2R7, 2015.

  109. RDKit 2020.03; Open-Source Cheminformatics Software: San Francisco, CA, USA, 2018. http://www.rdkit.org. Accessed Mar 2020

  110. Rogers D, Hahn M (2010) Extended-connectivity fingerprints. J Chem Inf Model 50:742–754. https://doi.org/10.1021/ci100050t

    Article  CAS  PubMed  Google Scholar 

  111. Harrison R. Wermuth, Talel Badri, Veronica Takov (2020) Montelukast. https://www.ncbi.nlm.nih.gov/books/NBK459301/ Accessed Sep 2020

  112. Gómez-Bombarelli R, Wei JN, Duvenaud D, Hernández-Lobato JM, Sánchez-Lengeling B, Sheberla D, Aguilera-Iparraguirre J, Hirzel TD, Adams RP, Aspuru-Guzik A (2018) Automatic chemical design using a data-driven continuous representation of molecules. ACS Cent Sci 4:268–276. https://doi.org/10.1021/acscentsci.7b00572

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  113. Schwalbe-Koda D, Gómez-Bombarelli R (2020) Generative models for automatic chemical design. Machine learning meets quantum physics. Springer International Publishing, Cham, pp 445–467

    Chapter  Google Scholar 

  114. Xu Y, Lin K, Wang S, Wang L, Cai C, Song C, Lai L, Pei J (2019) Deep learning for molecular generation. Future Med Chem 11:567–597. https://doi.org/10.4155/fmc-2018-0358

    Article  CAS  PubMed  Google Scholar 

  115. Mittal S, Vaishay S (2019) A survey of techniques for optimizing deep learning on gpus. J Syst Archit 99:101635. https://doi.org/10.1016/j.sysarc.2019.101635

    Article  Google Scholar 

  116. Bengio Y, Simard P, Frasconi P (1994) Learning long-term dependencies with gradient descent is difficult. IEEE Trans Neural Networks 5(2):157–166

    Article  CAS  PubMed  Google Scholar 

  117. Yamak, Peter T., Yujian, Li, Gadosey, Pius K (2019) A comparison between ARIMA, LSTM, and GRU for time series forecasting. In: Proceedings of the 2019 2nd international conference on algorithms, computing and artificial intelligence. Association for Computing Machinery, New York, pp 49–55. https://doi.org/https://doi.org/10.1145/3377713.3377722

  118. Mandhana V and Taware R (2020) De Novo Drug Design Using Self Attention Mechanism. In: Proceedings of the 35th Annual ACM Symposium on Applied Computing. Association for Computing Machinery, New York, pp 8–12. https://doi.org/10.1145/3341105.3374024

  119. Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, Grisel O, Blondel M, Prettenhofer P, Weiss R, Dubourg V, Vanderplas J, Passos A, Cournapeau D, Brucher M, Perrot M, Duchesnay E (2011) Scikit-learn: machine learning in {P}ython. J Mach Learn Res 12:2825–2830

    Google Scholar 

  120. Daylight Theory Manual (2006) SMARTS - A Language for Describing Molecular Patterns. https://www.ics.uci.edu/~dock/manuals/DaylightTheoryManual/theory.smarts.html. Accessed Sep 2020

  121. Lynch KR, O’Neill GP, Liu Q, Im D-S, Sawyer N, Metters KM, Coulombe N, Abramovitz M, Figueroa DJ, Zeng Z, Connolly BM, Bai C, Austin CP, Chateauneuf A, Stocco R, Greig GM, Kargman S, Hooks SB, Hosfield E, Williams DL, Ford-Hutchinson AW, Caskey CT, Evans JF (1999) Characterization of the human cysteinyl leukotriene cyslt1 receptor. Nature 399:789–793. https://doi.org/10.1038/21658

    Article  CAS  PubMed  Google Scholar 

  122. Itadani S, Takahashi S, Ima M, Sekiguchi T, Fujita M, Nakayama Y, Takeuchi J (2014) Discovery of highly potent dual cyslt1 and cyslt2 antagonist. ACS Med Chem Lett 5:1230–1234. https://doi.org/10.1021/ml500298y

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  123. Pytorch Available at: https://github.com/pytorch/pytorch. Accessed Sep 2020

  124. Andrews EG, Antognoli GW, Breslow R, Carta MP, Carty TJ, Chambers RJ, Cheng JB, Cohan VL, Collins JL, Damon DB, Delehunt J, Eggler JF, Eskra JD, Freiert KW, Hada WA, Marfat A, Masamune H, Melvin LS, Mularski CJ, Naclerio BA, Pazoles CJ, Pillar JS, Rappach LA, Reiche P, Rusek FW, Sherman H, Shirley JT, Sweeney FJ, Tickner JE, Watson JW, Wright CF (1995) Synthesis and pharmacological profile of two novel heterocyclic chromanols cp-80,798 and cp-85,958 as potent ltd4 receptor antagonists. Bioorganic Med Chem Lett 5:1365–1370. https://doi.org/10.1016/0960-894X(95)00225-I

    Article  CAS  Google Scholar 

Download references

Acknowledgements

This work was supported by the National Natural Science Foundation of China (21675010) and ‘Chemical Grid Project’ of Beijing University of Chemical Technology. We thank Molecular Networks GmbH, Erlangen, Germany, for providing the programs CORINA Symphony.

Author information

Authors and Affiliations

  1. State Key Laboratory of Chemical Resource Engineering, Department of Pharmaceutical Engineering, University of Chemical Technology, Beijing, People’s Republic of China

    Hongzhao Wang, Zijian Qin & Aixia Yan

Authors
  1. Hongzhao Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Zijian Qin
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Aixia Yan
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Aixia Yan.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary file1 (PDF 1032kb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Wang, H., Qin, Z. & Yan, A. Classification models and SAR analysis on CysLT1 receptor antagonists using machine learning algorithms. Mol Divers 25, 1597–1616 (2021). https://doi.org/10.1007/s11030-020-10165-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11030-020-10165-4

Keywords

Search

Navigation