Quantitative structure–activity relationships to predict sweet and non-sweet tastes

  • Cristian Rojas
  • Davide Ballabio
  • Viviana Consonni
  • Piercosimo Tripaldi
  • Andrea Mauri
  • Roberto Todeschini
Regular Article
Part of the following topical collections:
  1. CHITEL 2015 - Torino - Italy


The aim of this work was the calibration and validation of mathematical models based on a quantitative structure–activity relationship approach to discriminate sweet, tasteless and bitter molecules. The sweet-tasteless and the sweet-bitter datasets included 566 and 508 compounds, respectively. A total of 3763 conformation-independent Dragon molecular descriptors were calculated and subsequently reduced through both unsupervised reduction and supervised selection coupled with the k-nearest neighbors classification technique. A model based on nine descriptors was retained as the optimal one for sweet and tasteless molecules, while a model based on four descriptors was calibrated for the sweetness-bitterness dataset. Models were properly validated through cross-validation and external test sets. The applicability domain of models was investigated, and the interpretation of the role of the molecular descriptors in classifying sweet and non-sweet tastes was evaluated. The classification and the performance of the models presented in this paper are simple but accurate. They are based on a relatively small number of descriptors and a straightforward classification approach. The results presented here indicate that the proposed models can be used to accurately select new compounds as potential sweetener candidates.


QSAR k-Nearest neighbors Classification Sweetness 



Cristian Rojas is grateful for his PhD Fellowship from the National Secretary of Higher Education, Science, Technology and Innovation (SENESCYT) from the Republic of Ecuador, as well as for the financial support provided by the Ministry of Foreign Affairs and International Cooperation (FARNESINA) from the Italian Government for the PhD research conducted at the University of Milano-Bicocca.

Supplementary material

214_2016_1812_MOESM1_ESM.xlsx (64 kb)
Supplementary material 1 (XLSX 63 kb)


  1. 1.
    Shallenberger RS (1993) Taste chemistry. Springer Science & Business Media, BerlinCrossRefGoogle Scholar
  2. 2.
    Hugot E, Jenkins GH (1972) Handbook of cane sugar engineering, vol 114. Elsevier, PhiladelphiaGoogle Scholar
  3. 3.
    Asadi M (2006) Beet-sugar handbook. Wiley, NewYorkCrossRefGoogle Scholar
  4. 4.
    Birch GG (1999) Modulation of sweet taste. BioFactors 9(1):73–80CrossRefGoogle Scholar
  5. 5.
    deMan JM (1999) Principles of food chemistry, 3rd edn. Berlin, SpringerCrossRefGoogle Scholar
  6. 6.
    Oertly E, Myers RG (1919) A new theory relating constitution to taste. Simple relations between the constitution of aliphatic compounds and their sweet taste. J Am Chem Soc 41(6):855–867CrossRefGoogle Scholar
  7. 7.
    Shallenberger RS, Acree TE (1967) Molecular theory of sweet taste. Nature 216:480–482CrossRefGoogle Scholar
  8. 8.
    Kier LB (1972) A molecular theory of sweet taste. J Pharm Sci 61(9):1394–1397CrossRefGoogle Scholar
  9. 9.
    Nofre C, Tinti J-M (1996) Sweetness reception in man: the multipoint attachment theory. Food Chem 56(3):263–274CrossRefGoogle Scholar
  10. 10.
    Ellis JW (1995) Overview of sweeteners. J Chem Educ 72(8):671CrossRefGoogle Scholar
  11. 11.
    Katritzky AR, Lobanov VS, Karelson M (1995) QSPR: the correlation and quantitative prediction of chemical and physical properties from structure. Chem Soc Rev 24:279–287CrossRefGoogle Scholar
  12. 12.
    Trinajstic N (1992) Chemical graph theory. CRC Press, Boca RatonGoogle Scholar
  13. 13.
    Diudea MV (2001) QSPR/QSAR studies by molecular descriptors. Nova Science Publishers, New YorkGoogle Scholar
  14. 14.
    Todeschini R, Consonni V (2009) Molecular descriptors for chemoinformatics, vol 2. Wiley-VCH, WeinheimCrossRefGoogle Scholar
  15. 15.
    Iwamura H (1980) Structure-taste relationship of perillartine and nitro-and cyanoaniline derivatives. J Med Chem 23(3):308–312CrossRefGoogle Scholar
  16. 16.
    van der Wel H, van der Heijden A, Peer H (1987) Sweeteners. Food Rev Int 3(3):193–268CrossRefGoogle Scholar
  17. 17.
    Kier LB (1980) Molecular structure influencing either a sweet or bitter taste among aldoximes. J Pharm Sci 69(4):416–419CrossRefGoogle Scholar
  18. 18.
    Takahashi Y, Miyashita Y, Tanaka Y, Abe H, Sasaki S (1982) A consideration for structure-taste correlations of perillartines using pattern-recognition techniques. J Med Chem 25(10):1245–1248CrossRefGoogle Scholar
  19. 19.
    Takahashi Y, Abe H, Miyashita Y, Tanaka Y, Hayasaka H, Sasaki SI (1984) Discriminative structural analysis using pattern recognition techniques in the structure-taste problem of perillartines. J Pharm Sci 73(6):737–741CrossRefGoogle Scholar
  20. 20.
    Miyashita Y, Takahashi Y, Takayama C, Ohkubo T, Funatsu K, Sasaki S-I (1986) Computer-assisted structure/taste studies on sulfamates by pattern recognition methods. Anal Chim Acta 184:143–149CrossRefGoogle Scholar
  21. 21.
    Miyashita Y, Takahashi Y, Takayama C, Sumi K, Nakatsuka K, Ohkubo T, Abe H, Sasaki S (1986) Structure-taste correlation of L-aspartyl dipeptides using the SIMCA method. J Med Chem 29(6):906–912CrossRefGoogle Scholar
  22. 22.
    Okuyama T, Miyashita Y, Kanaya S, Katsumi H, S-i Sasaki, Randić M (1988) Computer assisted structure-taste studies on sulfamates by pattern recognition method using graph theoretical invariants. J Comput Chem 9(6):636–646CrossRefGoogle Scholar
  23. 23.
    Spillane WJ, McGlinchey G (1981) Structure-activity studies on sulfamate sweeteners II: semiquantitative structure-taste relationship for sulfamate (RNHSO3 ) sweeteners-the role of R. J Pharm Sci 70(8):933–935CrossRefGoogle Scholar
  24. 24.
    Spillane WJ, McGlinchey G, Muircheartaigh IÓ, Benson GA (1983) Structure-activity studies on sulfamate sweetners III: structure-taste relationships for heterosulfamates. J Pharm Sci 72(8):852–856CrossRefGoogle Scholar
  25. 25.
    Spillane WJ, Sheahan MB (1989) Semi-quantitative and quantitative structure-taste relationships for carboand hetero-sulphamate (RNHSO3 ) sweeteners. J Chem Soc, Perkin Trans 2(7):741–746CrossRefGoogle Scholar
  26. 26.
    Spillane WJ, Sheahan M (1991) Structure-taste relationships for sulfamate sweeteners (RNHSO3 ). Phosphorus Sulfur Silicon Relat Elem 59(1–4):255–258CrossRefGoogle Scholar
  27. 27.
    Spillane WJ, Sheahan MB, Ryder CA (1993) Synthesis and taste properties of sodium disubstituted phenylsulfamates. Structure-taste relationships for sweet and bitter/sweet sulfamates. Food Chem 47(4):363–369CrossRefGoogle Scholar
  28. 28.
    Drew MGB, Wilden GRH, Spillane WJ, Walsh RM, Ryder CA, Simmie JM (1998) Quantitative structure-activity relationship studies of sulfamates RNHSO3Na: distinction between sweet, sweet-bitter, and bitter molecules. J Agric Food Chem 46(8):3016–3026CrossRefGoogle Scholar
  29. 29.
    Spillane WJ, Ryder CA, Curran PJ, Wall SN, Kelly LM, Feeney BG, Newell J (2000) Development of structure-taste relationships for sweet and non-sweet heterosulfamates. J Chem Soc Perkin Trans 2(7):1369–1374CrossRefGoogle Scholar
  30. 30.
    Spillane WJ, Feeney BG, Coyle CM (2002) Further studies on the synthesis and tastes of monosubstituted benzenesulfamates. A semi-quantitative structure-taste relationship for the meta-compounds. Food Chem 79(1):15–22CrossRefGoogle Scholar
  31. 31.
    Spillane WJ, Kelly LM, Feeney BG, Drew MG, Hattotuwagama CK (2003) Synthesis of heterosulfamates. Search for structure-taste relationships. Arkivoc 7:297–309Google Scholar
  32. 32.
    Kelly DP, Spillane WJ, Newell J (2005) Development of structure-taste relationships for monosubstituted phenylsulfamate sweeteners using classification and regression tree (CART) analysis. J Agric Food Chem 53(17):6750–6758CrossRefGoogle Scholar
  33. 33.
    Spillane WJ, Kelly DP, Curran PJ, Feeney BG (2006) Structure-taste relationships for disubstituted phenylsulfamate tastants using classification and regression tree (CART) Analysis. J Agric Food Chem 54(16):5996–6004CrossRefGoogle Scholar
  34. 34.
    Spillane WJ, Coyle CM, Feeney BG, Thompson EF (2009) Development of structure-taste relationships for thiazolyl-, benzothiazolyl-, and thiadiazolylsulfamates. J Agric Food Chem 57(12):5486–5493CrossRefGoogle Scholar
  35. 35.
    Spillane WJ (1993) Structure taste studies of sulphamates. In: Mathlouthi M, Kanters JA, Birch GG (eds) Sweet-taste chemoreception. Elsevier Science Publishers, Philadelphia, p 283Google Scholar
  36. 36.
    Spillane WJ, Ryder CA, Walsh MR, Curran PJ, Concagh DG, Wall SN (1996) Sulfamate sweeteners. Food Chem 56(3):255–261CrossRefGoogle Scholar
  37. 37.
    Walters DE (2006) Analysing and predicting properties of sweet-tasting compounds. In: Spillane WJ (ed) Optimising sweet taste in foods. pp 283–291Google Scholar
  38. 38.
    Rojas C, Duchowicz PR, Pis Diez R, Tripaldi P (2016) Applications of quantitative structure-relative sweetness relationships in food chemistry. In: Mercader AG, Duchowicz PR, Sivakumar PM (eds) Chemometrics applications and research: QSAR in medicinal chemistry. CRC Press, Taylor & Francis Group, pp 317–339Google Scholar
  39. 39.
    van der Heijden A (1997) Historical overview on structure-activity relationships among sweeteners. Pure Appl Chem 69(4):667–674Google Scholar
  40. 40.
    Spillane W, Malaubier J-B (2014) Sulfamic acid and its N-and O-substituted derivatives. Chem Rev 114(4):2507–2586CrossRefGoogle Scholar
  41. 41.
    Organisation for Economic Co-operation and Development (2007) Guidance document on the validation of (quantitative)structure-activity relationships [(Q)SAR] models. OECD Publishing, ParisGoogle Scholar
  42. 42.
    Arnoldi A, Bassoli A, Borgonovo G, Drew MG, Merlini L, Morini G (1998) Sweet isovanillyl derivatives: synthesis and structure-taste relationships of conformationally restricted analogues. J Agric Food Chem 46(10):4002–4010CrossRefGoogle Scholar
  43. 43.
    Arnoldi A, Bassoli A, Borgonovo G, Merlini L (1995) Synthesis and sweet taste of optically active (−)-haematoxylin and of some (±)-haematoxylin derivatives. J Chem Soc Perkin Trans 1(19):2447–2453CrossRefGoogle Scholar
  44. 44.
    Arnoldi A, Bassoli A, Borgonovo G, Merlini L, Morini G (1997) Synthesis and structure-activity relationships of sweet 2-benzoylbenzoic acid derivatives. J Agric Food Chem 45(6):2047–2054CrossRefGoogle Scholar
  45. 45.
    Arnoldi A, Bassoli A, Merlini L (1996) Progress in isovanillyl sweet compounds. Food Chem 56(3):247–253CrossRefGoogle Scholar
  46. 46.
    Arnoldi A, Bassoli A, Merlini L, Ragg E (1991) Isovanillyl sweeteners. Synthesis, conformational analysis, and structure-activity relationship of some sweet oxygen heterocycles. J Chem Soc Perkin Trans 2(9):1399–1406CrossRefGoogle Scholar
  47. 47.
    Arnoldi A, Bassoli A, Merlini L, Ragg E (1993) Isovanillyl sweeteners. Synthesis and sweet taste of sulfur heterocycles. J Chem Soc Perkin Trans 1(12):1359–1366CrossRefGoogle Scholar
  48. 48.
    Bassoli A, Borgonovo G, Drew MG, Merlini L (2000) Enantiodifferentiation in taste perception of isovanillic derivatives. Tetrahedron Asymmetry 11(15):3177–3186CrossRefGoogle Scholar
  49. 49.
    Bassoli A, Drew MGB, Hattotuwagama CK, Merlini L, Morini G, Wilden GRH (2001) Quantitative structure-activity relationships of sweet isovanillyl derivatives. Quant Struct-Act Relat 20(1):3–16CrossRefGoogle Scholar
  50. 50.
    Belitz H-D, Grosch W, Schieberle P (2009) Food chemistry, 4th edn. Springer-Verlag, HeidelbergGoogle Scholar
  51. 51.
    Nanayakkara NPD, Hussain RA, Pezzuto JM, Soejarto DD, Kinghorn AD (1988) An intensely sweet dihydroflavonol derivative based on a natural product lead compound. J Med Chem 31(6):1250–1253CrossRefGoogle Scholar
  52. 52.
    O’Brien-Nabors L (2001) Alternative sweeteners, 3rd edn. New York, Marcel Dekker IncGoogle Scholar
  53. 53.
    Yamato M, Hashigaki K (1979) Chemical structure and sweet taste of isocoumarins and related compounds. Chem Senses 4(1):35–47CrossRefGoogle Scholar
  54. 54.
    Yang X, Chong Y, Yan A, Chen J (2011) In-silico prediction of sweetness of sugars and sweeteners. Food Chem 128(3):653–658CrossRefGoogle Scholar
  55. 55.
    Zhong M, Chong Y, Nie X, Yan A, Yuan Q (2013) Prediction of sweetness by multilinear regression analysis and support vector machine. J Food Sci 78(9):S1445–S1450CrossRefGoogle Scholar
  56. 56.
    Paulus K, Reisch AM (1980) The influence of temperature on the threshold values of primary tastes. Chem Senses 5(1):11–21CrossRefGoogle Scholar
  57. 57.
    Open Babel, Open Babel: The Open Source Chemistry Toolbox. http://openbabel.org/
  58. 58.
    Berthold M, Cebron N, Dill F, Gabriel T, Kötter T, Meinl T, Ohl P, Sieb C, Thiel K, Wiswedel B (2008) KNIME: the konstanz information miner. In: Preisach C, Burkhardt H, Schmidt-Thieme L, Decker R (eds) Data analysis, machine learning and applications. Studies in classification, data analysis, and knowledge organization. Springer, Berlin Heidelberg, pp 319–326CrossRefGoogle Scholar
  59. 59.
    Hypercube, Inc., HyperChem. http://www.hyper.com
  60. 60.
    TALETE, srl., Dragon (version 6) (2015). Software for Molecular Descriptor Calculation, http://www.talete.mi.it/
  61. 61.
    Pearlman RS (1993) 3D molecular structures: generation and use in 3D searching. In: Kubinyi H (ed) 3D QSAR in drug design. Theory and applications, Springer Science & Business Media, pp 41–79Google Scholar
  62. 62.
    Doweyko AM (2004) 3D-QSAR illusions. J Comput Aided Mol Des 18(7–9):587–596CrossRefGoogle Scholar
  63. 63.
    Hechinger M, Leonhard K, Marquardt W (2012) What is wrong with quantitative structure-property relations models based on three-dimensional descriptors? J Chem Inf Model 52(8):1984–1993CrossRefGoogle Scholar
  64. 64.
    Kowalski B, Bender C (1972) k-Nearest neighbor classification rule (pattern recognition) applied to nuclear magnetic resonance spectral interpretation. Anal Chem 44(8):1405–1411CrossRefGoogle Scholar
  65. 65.
    Ballabio D, Consonni V, Mauri A, Claeys-Bruno M, Sergent M, Todeschini R (2014) A novel variable reduction method adapted from space-filling designs. Chemometr Intell Lab Syst 136:147–154CrossRefGoogle Scholar
  66. 66.
    Leardi R, Gonzalez AL (1998) Genetic algorithms applied to feature selection in PLS regression: how and when to use them. Chemometr Intell Lab Syst 41(2):195–207CrossRefGoogle Scholar
  67. 67.
    Ballabio D, Consonni V (2013) Classification tools in chemistry. Part 1: linear models. PLS-DA. Anal Methods 5(16):3790–3798CrossRefGoogle Scholar
  68. 68.
    Wold S, Esbensen K, Geladi P (1987) Principal component analysis. Chemometr Intell Lab Syst 2(1):37–52CrossRefGoogle Scholar
  69. 69.
    Jolliffe IT (1986) Principal component analysis. Springer Science + Business Media, BerlinCrossRefGoogle Scholar
  70. 70.
    Krzanowski W (1988) Principles of multivariate analysis: a user’s perspective. Oxford University Press, OxfordGoogle Scholar
  71. 71.
    Bro R, Smilde AK (2014) Principal component analysis. Anal Methods 6(9):2812–2831CrossRefGoogle Scholar
  72. 72.
    Sahigara F, Mansouri K, Ballabio D, Mauri A, Consonni V, Todeschini R (2012) Comparison of different approaches to define the applicability domain of QSAR models. Molecules 17(5):4791–4810CrossRefGoogle Scholar
  73. 73.
    Sahigara F, Ballabio D, Todeschini R, Consonni V (2013) Defining a novel k-nearest neighbours approach to assess the applicability domain of a QSAR model for reliable predictions. J Cheminfor 5:27CrossRefGoogle Scholar
  74. 74.
    Cassotti M, Consonni V, Mauri A, Ballabio D (2014) Validation and extension of a similarity-based approach for prediction of acute aquatic toxicity towards Daphnia magna. SAR QSAR Environ Res 25(12):1013–1036CrossRefGoogle Scholar
  75. 75.
    Cassotti M, Ballabio D, Consonni V, Mauri A, Tetko IV, Todeschini R (2014) Prediction of acute aquatic toxicity toward Daphnia magna by using the GA-kNN method. Altern Lab Anim 42:31–41Google Scholar
  76. 76.
    Cassotti M, Ballabio D, Todeschini R, Consonni V (2015) A similarity-based QSAR model for predicting acute toxicity towards the fathead minnow (Pimephales promelas). SAR QSAR Environ Res 26(3):217–243CrossRefGoogle Scholar
  77. 77.
    Ballabio D (2015) A MATLAB toolbox for principal component analysis and unsupervised exploration of data structure. Chemometr Intell Lab Syst 149:1–9CrossRefGoogle Scholar
  78. 78.
    MathWorks: Natick, MatLab (version (2011). http://www.mathworks.com
  79. 79.
    Carhart RE, Smith DH, Venkataraghavan R (1985) Atom pairs as molecular features in structure-activity studies: definition and applications. J Chem Inf Comput Sci 25(2):64–73CrossRefGoogle Scholar
  80. 80.
    Birch GG, Karim R, Lopez A (1994) Novel aspects of structure-activity relationships in sweet taste chemoreception. Food Qual Prefer 5(1):87–93CrossRefGoogle Scholar
  81. 81.
    Rojas C, Tripaldi P, Duchowicz PR (2016) A new QSPR study on relative sweetness. Int J Quant Struct Prop Relatsh 1(1):76–90Google Scholar
  82. 82.
    Ghose AK, Viswanadhan VN, Wendoloski JJ (1998) Prediction of hydrophobic (lipophilic) properties of small organic molecules using fragmental methods: an analysis of ALOGP and CLOGP methods. J Phys Chem A 102(21):3762–3772CrossRefGoogle Scholar
  83. 83.
    Baek N-I, Chung M-S, Shamon L, Kardono LBS, Tsauri S, Padmawinata K, Pezzuto JM, Soejarto DD, Kinghorn AD (1993) Selligueain A, a novel highly sweet proanthocyanidin from the rhizomes of Selliguea feei. J Nat Prod 56(9):1532–1538CrossRefGoogle Scholar
  84. 84.
    Birch GG (1987) Sweetness and sweeteners. Endeavour 11(1):21–24CrossRefGoogle Scholar
  85. 85.
    Birch G, Mylvaganam A (1976) Evidence for the proximity of sweet and bitter receptor sites. Nature 260:632–634CrossRefGoogle Scholar
  86. 86.
    van der Heijden A, van der Wel H, Peer HG (1985) Structure-activity relationships in sweeteners. I. Nitroanilines, sulphamates, oximes, isocoumarins and dipeptides. Chem Senses 10(1):57–72CrossRefGoogle Scholar
  87. 87.
    Katritzky AR, Petrukhin R, Perumal S, Karelson M, Prakash I, Desai N (2002) A QSPR study of sweetness potency using the CODESSA program. Croat Chem Acta 75(2):475–502Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  • Cristian Rojas
    • 1
    • 2
  • Davide Ballabio
    • 3
  • Viviana Consonni
    • 3
  • Piercosimo Tripaldi
    • 4
  • Andrea Mauri
    • 3
  • Roberto Todeschini
    • 3
  1. 1.Instituto de Investigaciones Fisicoquímicas Teóricas y Aplicadas INIFTA (CCT La Plata-CONICET, UNLP)La PlataArgentina
  2. 2.Decanato General de InvestigacionesUniversidad del AzuayCuencaEcuador
  3. 3.Milano Chemometrics and QSAR Research Group, Department of Earth and Environmental SciencesUniversity of Milano-BicoccaMilanItaly
  4. 4.Laboratorio de Química-Física de Alimentos, Facultad de Ciencia y TecnologíaUniversidad del AzuayCuencaEcuador

Personalised recommendations