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Development of Structure-Taste Relationships for Thiazolyl-, Benzothiazolyl-, and Thiadiazolylsulfamates

机译:噻唑基,苯并噻唑基和硫二唑基磺酸盐的结构-味道关系的发展

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A total of 28 new five-membered aromatic ring thiazolyl-, benzothiazolyl-, and thiadiazolylsulfa-mates,: as their sodium salts, have been synthesized and combined with 30 known similar heterocyclic sulfamates to create a database for the study of structure-activity (taste) relationships (SARs) in this heterocyclic,subgroup, which is known to contain a somewhat disproportionate number of sweet compounds compared to other groups of tastants. A series of nine parameters (descriptors) to describe the properties of the sulfamate anions were calculated in Spartan Pro and HyperChem programs. These are the highest occupied molecular orbital (HOMO), lowest unoccupied molecular orbital (LUMO), length of the molecule, dipole moment, area, volume, E_(Solv),σ (from the literature), and log P. The taste data for all 58 compounds were categorized into three classes, namely, sweet (S), nonsweet (N), and nonsweet/sweet (N/S). Discriminant analysis only classified 44 of the 58 compounds correctly. Classification and regression tree analysis (CART) using the S_ Plus program proved highly effective, in that the derived tree correctly classified 46 compounds from a training set of 48 and, from a computer randomly selected test set of 10 compounds, 7 had their taste correctly predicted. A second tree was grown using the additional taste category N/S, and this tree also performed extremely well, with 8 of the 10 compounds in the test set correctly classified. These trees should be very reliable for predicting the tastes of other heterocyclic sulfamates, which belong to the subset used here.
机译:总共合成了28种新的五元芳环噻唑基,苯并噻唑基和噻二唑基磺酸盐,并将其与30种已知的类似杂环氨基磺酸盐组合在一起,以创建用于研究结构活性的数据库(味)(杂环)(SARs)在这个杂环亚组中的含量,与其他类型的促味剂相比,甜味化合物的含量不成比例。在Spartan Pro和HyperChem程序中计算了用于描述氨基磺酸根阴离子特性的一系列九个参数(描述符)。这些是最高占据分子轨道(HOMO),最低未占据分子轨道(LUMO),分子长度,偶极矩,面积,体积,E_(Solv),σ(来自文献)和logP。味觉数据所有58种化合物都分为三类,即甜(S),不甜(N)和不甜/甜(N / S)。判别分析仅正确分类了58种化合物中的44种。事实证明,使用S_Plus程序进行的分类和回归树分析(CART)非常有效,因为派生树从48个训练集中正确分类了46种化合物,从计算机随机选择的10个化合物测试集中,正确鉴定了7种化合物的味道预料到的。使用其他口味类别N / S种植了第二棵树,该树的表现也非常好,测试集中的10种化合物中有8种被正确分类。这些树对于预测其他杂环氨基磺酸盐的味道应该非常可靠,这些其他杂环氨基磺酸盐属于此处使用的子集。

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