Rojas Cristian, Todeschini Roberto, Ballabio Davide, Mauri Andrea, Consonni Viviana, Tripaldi Piercosimo, Grisoni Francesca
Instituto de Investigaciones Fisicoquímicas Teóricas y Aplicadas, CONICET, Universidad Nacional de La PlataLa Plata, Argentina.
Vicerrectorado de Investigaciones, Universidad del AzuayCuenca, Ecuador.
Front Chem. 2017 Jul 25;5:53. doi: 10.3389/fchem.2017.00053. eCollection 2017.
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 -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.
这项工作描述了一种基于先进分子相似性来预测化学物质甜度的新方法。所提出的定量结构-味觉关系(QSTR)模型是一个专家系统,其开发遵循了经济合作与发展组织(OECD)为验证(定量)构效关系(QSAR)所定义的五项原则。649个甜味和非甜味分子通过构象无关的扩展连接指纹(ECFP)和分子描述符进行描述。特别是,ECFP空间中的分子相似性与分子味觉表现出明显的关联,并被用于模型开发。对于位于味觉分配更困难的子空间中的分子,通过线性和局部方法(偏最小二乘判别分析和最近邻分类器)之间的共识进行建模。该专家系统通过蒙特卡罗程序和外部数据集进行了全面验证,与现有模型相比给出了令人满意的结果。此外,QSTR模型可用于更深入地理解分子结构与甜度之间的关系,并用于新型甜味剂的设计。