Skip to main content
SpringerLink
Log in

Prediction of aqueous solubility by treatment of COSMO-RS data with empirical solubility equations: the roles of global orbital cut-off and COSMO solvent radius

  • Regular Article
  • Published:
Theoretical Chemistry Accounts Aims and scope Submit manuscript

Abstract

Aqueous solubility values of (E)-2-(ethyl(4-((4-nitrophenyl)diazenyl)phenol)amino)ethanol [B1], (E)-2,2′-((4-((4-nitrophenyl)diazenyl)phenyl)azanediyl)diethanol [B2], (E)-2,2′-((3-methyl-4-((4-nitrophenyl)diazenyl)phenyl)azanediyl)diethanol [B3] and (E)-2-((4-((2,4-dinitrophenyl)diazenyl)phenyl)(ethyl)amino)ethanol [B4] were predicted by the treatment of relevant COSMO-RS data with Cramer et al. solubility equation (CSE) and general solubility equation (GSE). DMol3 computational code was employed for the study, where all calculations were carried out using VWN-BP level of theory with double numerical basis set containing polarization functions (DNP). Effects of global orbital cut-off and COSMO solvent radius (CSR) on the predicted results were examined. The results revealed that COSMO-RS data performed very well with both the CSE and GSE, but the latter exhibited a greater prediction strength on average. For nearly all the studied molecules, GSE calculated solubility (SGSE) was found to increase with orbital cut-off and reached an optimum value at a cut-off of 5.5 Å. SGSE values obtained at this and higher cut-off values studied are comparable to experimental solubility values, especially for B1, B3 and B4, while better results were obtained for B2 at lower cut-off values. CSE calculated solubility (SCSE) showed no constant trend with cut-off variation, but at cut-off values ≥ 7.0 Å the SCSE values compare well with the experimental values, especially in the cases of B2 and B3. For all the studied molecules, SGSE decreased with the increase in CSR and the most reliable CSR value for GSE was found to be 1.3 Å. On the contrary, SCSE increased with CSR and for B1 and B4, this increase was followed by a drop in predicted values at CSR > 1.3 Å. However, the best CSR value for CSE was found to be 0.5 Å for almost all the molecules. Our findings have shown that aqueous solubility (in mol/L) of azo dyes can be accurately predicted using CSE or GSE with some COSMO-RS data and that global orbital cut and COSMO solvent radius are essential parameters for accurate prediction.

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
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

References

  1. Klamt A, Schüürmann G (1993) COSMO: a new approach to dielectric screening in solvents with explicit expressions for the screening energy and its gradient. J Chem Soc Perkin Trans 2(5):799–805

    Article  Google Scholar 

  2. Andzelm J, Kölmel C, Klamt A (1995) Incorporation of solvent effects into density functional calculations of molecular energies and geometries. J Chem Phys 103(21):9312–9320

    Article  CAS  Google Scholar 

  3. Klamt A (2011) The COSMO and COSMO-RS solvation models. Wiley Interdiscip Rev Comput Mol Sci 1(5):699–709

    Article  CAS  Google Scholar 

  4. Klamt A (1995) Conductor-like screening model for real solvents: a new approach to the quantitative calculation of solvation phenomena. J Phys Chem 99(7):2224–2235

    Article  CAS  Google Scholar 

  5. Klamt A, Jonas V, Bürger T, Lohrenz JC (1998) Refinement and parametrization of COSMO-RS. J Phys Chem A 102(26):5074–5085

    Article  CAS  Google Scholar 

  6. Fujisawa M, Tsutsumi H, Kimura T (2011) Prediction of solubility of practically insoluble drugs in water/ethanol solvents using non-empirical methods. J Chem Pharma Res 3(3):750–758

    CAS  Google Scholar 

  7. Lotfi M, Moniruzzaman M, Sivapragasam M, Kandasamy S, Mutalib MA, Alitheen NB, Goto M (2017) Solubility of acyclovir in nontoxic and biodegradable ionic liquids: COSMO-RS prediction and experimental verification. J Mol Liq 243:124–131

    Article  CAS  Google Scholar 

  8. Song Z, Zeng Q, Zhang J, Cheng H, Chen L, Qi Z (2016) Solubility of imidazolium-based ionic liquids in model fuel hydrocarbons: a COSMO-RS and experimental study. J Mol Liq 224:544–550

    Article  CAS  Google Scholar 

  9. Lotfi M, Moniruzzaman M, Rajabi MS (2015) Predicting the solubility of pharmaceutical compound in ionic liquids using COSMO-RS model. In: Malaysian technical universities conference on engineering and technology 2015

  10. Guo Z, Lue B-M, Thomasen K, Meyer AS, Xu X (2007) Predictions of flavonoid solubility in ionic liquids by COSMO-RS: experimental verification, structural elucidation, and solvation characterization. Green Chem 9(12):1362–1373

    Article  CAS  Google Scholar 

  11. Mokrushina L, Buggert M, Smirnova I, Arlt W, Schomäcker R (2007) COSMO-RS and UNIFAC in prediction of micelle/water partition coefficients. Ind Eng Chem Res 46(20):6501–6509

    Article  CAS  Google Scholar 

  12. Sicaire A-G, Vian MA, Fine F, Carré P, Tostain S, Chemat F (2015) Experimental approach versus COSMO-RS assisted solvent screening for predicting the solubility of rapeseed oil. Oilseeds Fats Crops Lipids 22(4):D404

    Google Scholar 

  13. Klamt A, Eckert F, Reinisch J, Wichmann K (2016) Prediction of cyclohexane-water distribution coefficients with COSMO-RS on the SAMPL5 data set. J Comput Aided Mol Des 30(11):959–967. https://doi.org/10.1007/s10822-016-9927-y

    Article  CAS  PubMed  Google Scholar 

  14. Mustapha S, Okonkwo P, Waziri S (2013) Improvement of carbon dioxide absorption technology using conductor-like screening model for real solvents (COSMO-RS) method. J Environ Chem Ecotoxicol 5(4):96–105

    CAS  Google Scholar 

  15. Wittekindt C, Klamt A (2009) COSMO-RS as a predictive tool for lipophilicity. Mol Inform 28(8):874–877

    CAS  Google Scholar 

  16. Klamt A, Eckert F, Hornig M, Beck ME, Bürger T (2002) Prediction of aqueous solubility of drugs and pesticides with COSMO-RS. J Comput Chem 23(2):275–281

    Article  CAS  PubMed  Google Scholar 

  17. Eckert F, Klamt A (2006) Accurate prediction of basicity in aqueous solution with COSMO-RS. J Comput Chem 27(1):11–19

    Article  CAS  PubMed  Google Scholar 

  18. Tshepelevitsh S, Hernits K, Leito I (2018) Prediction of partition and distribution coefficients in various solvent pairs with COSMO-RS. J Comput Aided Mol Des 32:711–722. https://doi.org/10.1007/s10822-018-0125-y

    Article  CAS  PubMed  Google Scholar 

  19. Schröder B, Freire MG, Varanda FR, Marrucho IM, Santos LM, Coutinho JA (2011) Aqueous solubility, effects of salts on aqueous solubility, and partitioning behavior of hexafluorobenzene: experimental results and COSMO-RS predictions. Chemosphere 84(4):415–422

    Article  PubMed  Google Scholar 

  20. Wahab O, Olasunkanmi L, Govender K, Govender P (2018) DMol3/COSMO-RS prediction of aqueous solubility and reactivity of selected Azo dyes: effect of global orbital cut-off and COSMO segment variation. J Mol Liq 249:346–360

    Article  CAS  Google Scholar 

  21. Delley B (2000) From molecules to solids with the DMol3 approach. J Chem Phys 113(18):7756–7764

    Article  CAS  Google Scholar 

  22. DMol3 User Guide V (1990) 4.2. 1 May 8 2001, Density Functional Theory Electronic Structure Program, Accelrys Inc.;(b) B. Delley. J Chem Phys 92:508

  23. Delley B (1995) DMol, a standard tool for density functional calculations: review and advances. J Theor Comput Chem 2:221–254

    Article  CAS  Google Scholar 

  24. Klamt A, Eckert F (2004) Prediction of vapor liquid equilibria using COSMOtherm. Fluid Phase Equilibr 217(1):53–57

    Article  CAS  Google Scholar 

  25. Klamt A, Eckert F (2007) Prediction, fine tuning, and temperature extrapolation of a vapor liquid equilibrium using COSMOtherm. Fluid Phase Equilibr 260(2):183–189

    Article  CAS  Google Scholar 

  26. Inada Y, Orita H (2008) Efficiency of numerical basis sets for predicting the binding energies of hydrogen bonded complexes: evidence of small basis set superposition error compared to Gaussian basis sets. J Comput Chem 29(2):225–232

    Article  CAS  PubMed  Google Scholar 

  27. Luo Y, Yin S, Lai W, Wang Y (2014) Effects of global orbital cutoff value and numerical basis set size on accuracies of theoretical atomization energies. Theor Chem Acc 133(11):1580(1)–1580(11)

    Article  Google Scholar 

  28. Basiuk VA, Henao-Holguín LV (2013) Effects of orbital cutoff in DMol3 DFT calculations: a case study of meso-tetraphenylporphine–C60 complex. J Comput Theor Nanosci 10(5):1266–1272

    Article  CAS  Google Scholar 

  29. DMol3 Guide (2014) Material studio 8.0. San Diego, CA 92121 USA. http://nees.sci.upc.edu.cn/_upload/article/files/39/f5/5460e8894554bd75148145ba414e/188a6221-e993-431c-bf9e-28e3051fd772.pdf. Accessed 27 May 2019

  30. Mullins E, Liu Y, Ghaderi A, Fast SD (2008) Sigma profile database for predicting solid solubility in pure and mixed solvent mixtures for organic pharmacological compounds with COSMO-based thermodynamic methods. Ind Eng Chem Res 47(5):1707–1725

    Article  CAS  Google Scholar 

  31. Mullins E, Oldland R, Liu Y, Wang S, Sandler SI, Chen CC, Zwolak M, Seavey KC (2006) Sigma-profile database for using COSMO-based thermodynamic methods. Ind Eng Chem Res 45(12):4389–4415

    Article  CAS  Google Scholar 

  32. DMol3 Keyword Descriptions, Cerius2 Quantum 1 Modules (1998) Molecular Simulations, Inc. http://www.chem.cmu.edu/courses/09-560/docs/msi/quantum/D_DMol3Keywords.html#670836. Accessed 24 March 2019

  33. Bhat S, Purisima EO (2006) Molecular surface generation using a variable-radius solvent probe. Proteins Struct Funct Bioinform 62(1):244–261

    Article  CAS  Google Scholar 

  34. Thompson JD, Cramer CJ, Truhlar DG (2003) Predicting aqueous solubilities from aqueous free energies of solvation and experimental or calculated vapor pressures of pure substances. J Chem Phys 119(3):1661–1670

    Article  CAS  Google Scholar 

  35. Peterson DL, Yalkowsky SH (2001) Comparison of two methods for predicting aqueous solubility. J Chem Inf Comput Sci 41(6):1531–1534

    Article  CAS  PubMed  Google Scholar 

  36. Bird C (1954) The dyeing of acetate rayon with disperse dyes I-aqueous solubility and the influence of dispersing agents II-the relation between aqueous solubility and dyeing properties. J Soc Dyers Colour 70(2):68–77

    Article  CAS  Google Scholar 

  37. Boyd PD, Hodgson MC, Rickard CE, Oliver AG, Chaker L, Brothers PJ, Reed CA (1999) Selective supramolecular porphyrin/fullerene interactions. J Am Chem Soc 121(45):10487–10495

    Article  CAS  Google Scholar 

  38. Wang YB, Lin Z (2003) Supramolecular interactions between fullerenes and porphyrins. J Am Chem Soc 125(20):6072–6073

    Article  CAS  PubMed  Google Scholar 

  39. Materials Studio simulation environment (2016) Release 2016. Accelrys Software Inc, San Diego

    Google Scholar 

  40. Becke AD (1986) Density functional calculations of molecular bond energies. J Chem Phys 84(8):4524–4529

    Article  CAS  Google Scholar 

  41. Becke AD (1988) Density-functional exchange-energy approximation with correct asymptotic behavior. Phys Rev A 38(6):3098

    Article  CAS  Google Scholar 

  42. Becke AD (1992) Density-functional thermochemistry. II. The effect of the Perdew-Wang generalized-gradient correlation correction. J Chem Phys 97(12):9173–9177

    Article  CAS  Google Scholar 

  43. Becke AD (1993) Density-functional thermochemistry. III. The role of exact exchange. J Chem Phys 98(7):5648–5652

    Article  CAS  Google Scholar 

  44. Becke AD (1993) A new mixing of Hartree–Fock and local density-functional theories. J Chem Phys 98(2):1372–1377

    Article  CAS  Google Scholar 

  45. Perdew JP (1986) Density-functional approximation for the correlation energy of the inhomogeneous electron gas. Phys Rev B 33(12):8822

    Article  CAS  Google Scholar 

  46. Vosko SH, Wilk L, Nusair M (1980) Accurate spin-dependent electron liquid correlation energies for local spin density calculations: a critical analysis. Can J Phys 58(8):1200–1211

    Article  CAS  Google Scholar 

  47. Benedek N, Snook I, Latham K, Yarovsky I (2005) Application of numerical basis sets to hydrogen bonded systems: a density functional theory study. J Chem Phys 122(14):144102

    Article  CAS  PubMed  Google Scholar 

  48. Yalkowsky SH, Valvani SC (1980) Solubility and partitioning I: solubility of nonelectrolytes in water. J Pharm Sci 69(8):912–922

    Article  CAS  PubMed  Google Scholar 

Download references

Acknowledgements

The authors express gratitude to the Department of Applied Chemistry-Centre of Nanomaterials Science Research (CNSR), Faculty of Science-University of Johannesburg (TTK14052167682) for providing financial aid, and the Centre for High Performance Computing (CHPC, South Africa) for providing the needed computational resources for this work.

Author information

Authors and Affiliations

  1. Department of Applied Chemistry, University of Johannesburg, Doornfontein Campus, P. O. Box 17011, Johannesburg, 2028, South Africa

    Olaide O. Wahab, Krishna K. Govender & Penny P. Govender

  2. Department of Chemistry, Emmet A. Dennis College of Natural Science, Cuttington University, Suakoko, Liberia

    Olaide O. Wahab

  3. Department of Chemistry, Faculty of Science, Obafemi Awolowo University, Ile-Ife, 220005, Nigeria

    Lukman O. Olasunkanmi

  4. Council for Scientific and Industrial Research, National Integrated Cyber Infrastructure, Centre for High Performance Computing, 15 Lower Hope Road, Rosebank, Cape Town, 7700, South Africa

    Krishna K. Govender

Authors
  1. Olaide O. Wahab
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Lukman O. Olasunkanmi
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Krishna K. Govender
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Penny P. Govender
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Penny P. Govender.

Ethics declarations

Conflict of interest

The authors declare that they have no conflicts of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Electronic supplementary material

Below is the link to the electronic supplementary material.

214_2019_2470_MOESM1_ESM.docx

Tables S1–S4 present the calculated solvation free energy (SFE), vapour pressure (PS), octanol–water partition coefficient (Log P) and aqueous solubility (SCSE and SGSE) at 25 °C for B1–B4, respectively. Supplementary material 1 (DOCX 40 kb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Wahab, O.O., Olasunkanmi, L.O., Govender, K.K. et al. Prediction of aqueous solubility by treatment of COSMO-RS data with empirical solubility equations: the roles of global orbital cut-off and COSMO solvent radius. Theor Chem Acc 138, 80 (2019). https://doi.org/10.1007/s00214-019-2470-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s00214-019-2470-x

Keywords

Search

Navigation