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 

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