Computer assisted structure–taste studies on sulfamates by pattern recognition method using graph theoretical invariants

Abstract

Structure-taste relationships for 25 acyclic and 20 cyclic carbosulfamates were investigated by means of pattern recognition using different graph theoretical invariants as molecular substituent descriptors. The SIMCA method was used to classify the compounds into sweet and nonsweet classes. All selected graph theoretical invariants that are related to the "rooted" vertex were found to give promising results. Using the weighted path numbers and self-returning walks for the rooted atom as descriptors of substituents, we found 87% of acyclic compounds were correctly classified. Using the atomic path numbers for the rooted atom as descriptors of substituents, we found 81% of cyclic compounds were correctly classified. These results are better than previously used shape and size substituent descriptors. It may be concluded that the graph theoretical descriptors have great potential in encoding structure components in structure-activity studies (SAR) studies.

A QSTR-Based Expert System to Predict Sweetness of Molecules

“…The curated TastesDB dataset consisted of 649 molecules divided into two subsets of 435 sweet and 214 non-sweet (133 tasteless and 81 bitter) compounds, respectively (Table S1 ). 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.…”

“…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 ).…”

Chemometric QSAR studies of antifungal azoxy compounds

“…However, it is important to assess the reliability of interpolated and extrapolated activities with statistical methods. It is possible to evaluate a measure of reliability for the predicted activity by calculating the sample RSD (Residual Standard Deviation) and comparing it with the RSD value of the calibration set [7,22]. If a sample RSD is smaller than the RSD value of the calibration set, the predicted activity is more reliable.…”

Estudio y aplicación de técnicas matemáticas de la teoría QSAR–QSPR en química analítica y química de los alimentos

“…Para enfrentar estos inconvenientes, los químicos han desarrollado modelos matemáticos basados en la teoría QSAR/QSPR con la finalidad de predecir el dulzor de los compuestos y optimizar la síntesis los mismos. Además de los modelos QSAR previamente descritos para los gustos dulce-amargo, se han propuesto otros modelos que buscan discriminar entre compuestos dulces y no dulces de carbosulfamatos (Miyashita et al, 1986a;Okuyama et al, 1988) y otros derivados del sulfamato (Spillane & McGlinchey, 1981;Spillane & Sheahan, 1989;Spillane & Sheahan, 1991;Spillane et al, 1993;Spillane et al, 2000;Spillane et al, 2003;Spillane et al, 2009).…”

“…En la Tabla 5.12 se presentan los diversos modelos QSAR desarrollados para discriminar moléculas dulces y no dulces. En la mayoría de los casos estos modelos se han establecido mediante el uso de bases de datos con familias de compuestos homogéneas (Iwamura, 1980;Kier, 1980;Spillane & McGlinchey, 1981;Takahashi et al, 1982;Spillane et al, 1983;Takahashi et al, 1984;Miyashita et al, 1986a;Miyashita et al, 1986b;Okuyama et al, 1988;Spillane & Sheahan, 1989;Spillane & Sheahan, 1991;Spillane et al, 1993;Drew et al, 1998;Spillane et al, 2000;Spillane et al, 2002;Spillane et al, 2003;Spillane et al, 2009). Este hecho limita la generalización de tales modelos a diferentes tipos de compuestos, es decir, el dominio de aplicabilidad de los mismos es restringido.…”