4D-QSAR studies of CB2 cannabinoid receptor inverse agonists: a comparison to 3D-QSAR

  • Houpan Zhang
  • Qiaoli Lv
  • Weidong Xu
  • Xiaoping Lai
  • Ya Liu
  • Guogang TuEmail author
Original Research


Over the years QSAR methods have developed from 2D-QSAR to more complex 4D-QSAR which features freedom of alignment and conformational flexibility of individual ligands. This approach takes advantage of conformational ensemble profile (CEP) generated for individual compounds by molecular dynamics simulations. In present study, the 4D-QSAR methods called LQTAgrid-QSAR has been performed on a series of potent CB2 cannabinoid receptor inverse agonists. Step-wise method was used to select the most informative variables. Partial least squares (PLS) and multiple linear regression (MLR) methods were used for constructing the regression models. Y-randomization and leave-N-out cross-validation (LNO) were carried out to verify the robustness of the model and to analysis of the independent test set. Best 4D-QSAR model provided the following statistics: R2 = 0.862, q2LOO = 0.737, q2LNO = 0.719, R2Pred = 0.884 (PLS) and R2 = 0.863, q2LOO = 0.771, q2LNO = 0.761, R2Pred = 0.877 (MLR). The comparison of the 4D-QSAR to 3D-QSAR was performed.


4D-QSAR 3D-QSAR Molecular dynamic simulation CB2 inverse agonists 



The project was supported by the Jiangxi Province Science Foundation (20171BAB205104), the graduate innovation fund of Jiangxi Province (CX2016298).

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

Supplementary material

44_2019_2303_MOESM1_ESM.doc (542 kb)
Supplementary Information


  1. Andrade CH, Pasqualoto KFM, Ferreira EI, Hopfinger AJ (2010) 4D-QSAR: perspectives in drug design. Molecules 15:3281–3294CrossRefGoogle Scholar
  2. Berendsen HJC, Postma JPM, Vangunsteren WF, Dinola A, Haak JR (1984) Molecular-dynamics with coupling to an external bath. J Chem Phys 81:3684–3690CrossRefGoogle Scholar
  3. Clark RD (2003) Boosted leave-many-out cross-validation: the effect of training and test set diversity on pls statistics. J Comput Aided Mol Des 17:265–275CrossRefGoogle Scholar
  4. Darden T, York D, Pedersen L (1993) Particle mesh ewald - an N-Log(N) method for ewald sums in large systems. J Chem Phys 98:10089–10092CrossRefGoogle Scholar
  5. Di Marzo V, Bifulco M, De Petrocellis L (2004) The endocannabinoid system and its therapeutic exploitation. Nat Rev Drug Discov 3:771–784CrossRefGoogle Scholar
  6. Ghasemi JB, Safavi-Sohi R, Barbosa EG (2012) 4D-LQTA-QSAR and docking study on potent gram-negative specific lpxc inhibitors: a comparison to comfa modeling. Mol Divers 16:203–213CrossRefGoogle Scholar
  7. Ghasemi JB, Salahinejad M, Rofouei MK (2011) Review of the quantitative structure-activity relationship modelling methods on estimation of formation constants of macrocyclic compounds with different guest molecules. Supramol Chem 23:615–631CrossRefGoogle Scholar
  8. Golbraikh A, Tropsha A (2002) Beware of q(2)! J Mol Graph Model 20:269–276CrossRefGoogle Scholar
  9. Gramatica P, Chirico N, Papa E, Cassani S, Kovarich S (2013) Qsarins: a new software for the development, analysis, and validation of qsar mlr models. J Comput Chem 34:2121–2132CrossRefGoogle Scholar
  10. Hassinen T, Peräkylä M (2001) New energy terms for reduced protein models implemented in an off-lattice force field. J Comput Chem 22:1229–1242CrossRefGoogle Scholar
  11. Hopfinger AJ, Wang S, Tokarski JS, Jin BQ, Albuquerque M, Madhav PJ, Duraiswami C (1997) Construction of 3D-QSAR models using the 4D-QSAR analysis formalism. J Am Chem Soc 119:10509–10524CrossRefGoogle Scholar
  12. Howlett AC, Barth F, Bonner TI, Cabral G, Casellas P, Devane WA, Felder CC, Herkenham M, Mackie K, Martin BR, Mechoulam R, Pertwee RG (2002) International union of pharmacology. Xxvii. Classif cannabinoid Recept Pharmacol Rev 54:161–202Google Scholar
  13. Kiralj R, Ferreira MMC (2009) Basic validation procedures for regression models in QSAR and QSPR studies: theory and application. J Braz Chem Soc 20:770–787CrossRefGoogle Scholar
  14. Malfitano AM, Basu S, Maresz K, Bifulco M, Dittel BN (2014) What we know and do not know about the cannabinoid receptor 2 (CB2). Semin Immunol 26:369–379CrossRefGoogle Scholar
  15. Martins JPA, Barbosa EG, Pasqualoto KFM, Ferreira MMC (2009) LQTA-QSAR: a new 4D-QSAR methodology. J Chem Inf Model 49:1428–1436CrossRefGoogle Scholar
  16. Matsuda LA, Lolait SJ, Brownstein MJ, Young AC, Bonner TI (1990) Structure of a cannabinoid receptor and functional expression of the cloned cDNA. Nature 346:561–564CrossRefGoogle Scholar
  17. Munro S, Thomas KL, Abushaar M (1993) Molecular characterization of a peripheral receptor for cannabinoids. Nature 365:61–65CrossRefGoogle Scholar
  18. Patel P, Rajak H (2018) Development of hydroxamic acid derivatives as anticancer agent with the application of 3D-QSAR, docking and molecular dynamics simulations studies. Med Chem Res 27:2100–2115CrossRefGoogle Scholar
  19. Patil R, Sawant S (2015) Molecular dynamics guided receptor independent 4D QSAR studies of substituted coumarins as anticancer agents. Curr Comput Aided Drug Des 11:39–50CrossRefGoogle Scholar
  20. Pettersen EF, Goddard TD, Huang CC, Couch GS, Greenblatt DM, Meng EC, Ferrin TE (2004) UCSF chimera - a visualization system for exploratory research and analysis. J Comput Chem 25:1605–1612CrossRefGoogle Scholar
  21. Picone RP, Kendall DA (2015) From the bench, toward the clinic: therapeutic opportunities for cannabinoid receptor modulation. Mol Endocrinol 29:801–813CrossRefGoogle Scholar
  22. Shim J, Mackerell AD (2011) Computational ligand-based rational design: role of conformational sampling and force fields in model development. Medchemcomm 2:356–370CrossRefGoogle Scholar
  23. Tabrizi MA, Baraldi PG, Ruggiero E, Saponaro G, Baraldi S, Poli G, Tuccinardi T, Ravani A, Vincenzi F, Borea PA, Varani K (2016) Synthesis and structure activity relationship investigation of triazolo 1,5-a pyrimidines as CB2 cannabinoid receptor inverse agonists. Eur J Med Chem 113:11–27CrossRefGoogle Scholar
  24. Tosco P, Balle T (2011) Open3DQSAR: a new open-source software aimed at high-throughput chemometric analysis of molecular interaction fields. J Mol Model 17:201–208CrossRefGoogle Scholar
  25. Tosco P, Balle T, Shiri F (2011) Open3DALIGN: an open-source software aimed at unsupervised ligand alignment. J Comput Aided Mol Des 25:777–783CrossRefGoogle Scholar
  26. Uesawa Y, Mohri K (2010) Quantitative structure-activity relationship (QSAR) analysis of the inhibitory effects of furanocoumarin derivatives on cytochrome p450 3A activities. Pharmazie 65:41–46Google Scholar
  27. Van Der Spoel D, Lindahl E, Hess B, Groenhof G, Mark AE, Berendsen HJC (2005) Gromacs: fast, flexible, and free. J Comput Chem 26:1701–1718CrossRefGoogle Scholar
  28. Wold S, Sjostrom M, Eriksson L (2001) PLS-regression: a basic tool of chemometrics. Chemom Intell Lab Syst 58:109–130CrossRefGoogle Scholar

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© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of Medicinal Chemistry, School of Pharmaceutical ScienceNanChang UniversityNanchangChina
  2. 2.Department of Science and Education, JiangXi Key Laboratory of Translational Cancer ResearchJiangXi Cancer HospitalNanchangChina

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