Structure–Activity Studies on Sulfamate Sweetners III: Structure–Taste Relationships for Heterosulfamates

Spillane, William J.; McGlinchey, Grainne; Muircheartaigh, Iognáid Ó; Benson, G. Anthony

doi:10.1002/jps.2600720804

Cited by 32 publications

(19 citation statements)

References 13 publications

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“…QSTR studies regarding the prediction of the sweetness receptor-mediated taste were conducted by considering only homogeneous families of sweeteners (Iwamura, 1980; Kier, 1980; Spillane and McGlinchey, 1981; Takahashi et al, 1982, 1984; Spillane et al, 1983, 1993, 2000, 2002, 2003, 2006, 2009; Miyashita et al, 1986a,b; van der Wel et al, 1987; Okuyama et al, 1988; Spillane and Sheahan, 1989, 1991; Drew et al, 1998; Kelly et al, 2005), limiting their ability to predict the sweetness of other kinds of sweeteners. In order to generalize the predictiveness of the QSTR-based expert system, we used a dataset that covered a large chemical space of both sweet and non-sweet molecules.…”

Section: Methodsmentioning

confidence: 99%

“…Several Quantitative Structure-Taste Relationships (QSTRs) for predicting the sweetness of chemicals were proposed in the past years and are summarized in Table 1. The earlier work included compounds such as perillartine and aniline derivatives (Iwamura, 1980; van der Wel et al, 1987), sweet and bitter aldoxime derivatives (Kier, 1980), perillartine derivatives, aspartyl dipeptides, and carbosulfamates (Takahashi et al, 1982, 1984; Miyashita et al, 1986a,b; Okuyama et al, 1988), as well as sulfamate derivatives (Spillane and McGlinchey, 1981; Spillane et al, 1983, 1993, 2000, 2002, 2003, 2006, 2009; Spillane and Sheahan, 1989, 1991; Drew et al, 1998; Kelly et al, 2005). Moreover, two QSTR models to discriminate sweet, tasteless and bitter compounds have been proposed (Rojas et al, 2016a).…”

Section: Introductionmentioning

confidence: 99%

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A QSTR-Based Expert System to Predict Sweetness of Molecules

et al. 2017

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This work describes a novel approach based on advanced molecular similarity to predict the sweetness of chemicals. The proposed Quantitative Structure-Taste Relationship (QSTR) model is an expert system developed keeping in mind the five principles defined by the Organization for Economic Co-operation and Development (OECD) for the validation of (Q)SARs. The 649 sweet and non-sweet molecules were described by both conformation-independent extended-connectivity fingerprints (ECFPs) and molecular descriptors. In particular, the molecular similarity in the ECFPs space showed a clear association with molecular taste and it was exploited for model development. Molecules laying in the subspaces where the taste assignation was more difficult were modeled trough a consensus between linear and local approaches (Partial Least Squares-Discriminant Analysis and N-nearest-neighbor classifier). The expert system, which was thoroughly validated through a Monte Carlo procedure and an external set, gave satisfactory results in comparison with the state-of-the-art models. Moreover, the QSTR model can be leveraged into a greater understanding of the relationship between molecular structure and sweetness, and into the design of novel sweeteners.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A QSTR-Based Expert System to Predict Sweetness of Molecules

et al. 2017

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“…Taste data conclusions (S/N) for compounds 1 -33 are in ref. 10, for compounds 34 -56 in ref. 11 and for compounds 57 -101 in ref.…”

Section: Methodsmentioning

confidence: 99%

“…In previous work we have had some success in deriving structure-taste relationships (SARs) for the first 33 compounds, 10 then 56 compounds, 11 and more recently 101 compounds. 12 Each data set was examined using the Corey-Pauling-Koltun (CPK) parameters for the RNH portion of the heterosulfamate i.e.…”

Section: Introductionmentioning

confidence: 99%

“…The mathematical techniques of linear discriminant analysis (LDA) 10,11,12 and quadratic discriminant analysis (QDA) 10,11,12 and, more recently, Tree analysis 12 have been employed in studying the data sets. The LDA and QDA analysis worsened somewhat as the size of the data set grew and thus Tree analysis was introduced to try to improve the classifications.…”

Section: Introductionmentioning

confidence: 99%

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Synthesis of heterosulfamates. Search for structure-taste relationships

Spillane¹,

Kelly²,

Feeney³

et al. 2003

Arkivoc

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Thirty one new sodium heterosulfamates, RNHSO 3 Na, where the R portion contains mainly thiazole, benzothiazole, thiadiazole and pyridine ring structures, have been synthesized and their taste portfolios have been assessed. A database of 132 heterosulfamates (both open-chain and cyclic) has been formed by combining these new compounds with an existing set of 101 heterosulfamates which were previously synthesized and for which taste data are available.Simple descriptors have been obtained using (i) measurements with Corey-Pauling-Koltun (CPK) space-filling models giving x, y and z dimensions and a volume V CPK , (ii) calculated first order molecular connectivities ( 1 χ v ) and (iii) the calculated Spartan program parameters to obtain HOMO, LUMO energies, the solvation energy E solv and V SPARTAN . The techniques of linear (LDA) and quadratic (QDA) discriminant analysis and Tree analysis have then been employed to develop structure-taste relationships (SARs) that classify the sweet (S) and non-sweet (N) compounds into separate categories. In the LDA analysis 70 % of the compounds were correctly classified (this compares with 65 % when the smaller data set of 101 compounds was used) and in the QDA analysis 68 % were correctly classified (compared to 80 % previously). TheTree analysis correctly classified 81 % (compared to 86 % previously). An alternative Tree analysis derived using the Cerius2 program and a set of physicochemical descriptors correctly classified only 54 % of the compounds.

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