PDF(Pubmed)
Abstract：
The need to solvate and encapsulate hydro-sensitive molecules drives noticeable trends in the applications of cyclodextrins in the pharmaceutical industry, in foods, polymers, materials, and in agricultural science. Among them, β-cyclodextrin is one of the most used for the entrapment of phenolic acid compounds to mask the bitterness of wheat bran. In this regard, there is still a need for good data and especially for a robust predictive model that assesses the bitterness masking capabilities of β-cyclodextrin for various phenolic compounds. This study uses a dataset of 20 phenolic acids docked into the β-cyclodextrin cavity to generate three different binding constants. The data from the docking study were combined with topological, topographical, and quantum-chemical features from the ligands in a machine learning-based structure-activity relationship study. Three different models for each binding constant were computed using a combination of the genetic algorithm (GA) and multiple linear regression (MLR) approaches. The developed ML/QSAR models showed a very good performance, with high predictive ability and correlation coefficients of 0.969 and 0.984 for the training and test sets, respectively. The models revealed several factors responsible for binding with cyclodextrin, showing positive contributions toward the binding affinity values, including such features as the presence of six-membered rings in the molecule, branching, electronegativity values, and polar surface area.
摘要：
溶剂化和包封水敏分子的需求推动了环糊精在制药工业中应用的显著趋势。在食物中，聚合物，材料,和农业科学。其中,β-环糊精是最常用于包封酚酸化合物以掩盖麦麸的苦味的物质之一。在这方面,仍然需要良好的数据，尤其是评估β-环糊精对各种酚类化合物的苦味掩蔽能力的稳健预测模型。本研究使用对接到β-环糊精腔中的20种酚酸的数据集来产生三种不同的结合常数。对接研究的数据与拓扑相结合，地形,以及基于机器学习的结构-活性关系研究中配体的量子化学特征。使用遗传算法(GA)和多元线性回归(MLR)方法的组合计算每种结合常数的三种不同模型。开发的ML/QSAR模型表现出很好的性能，训练集和测试集具有很高的预测能力和0.969和0.984的相关系数，分别。模型揭示了与环糊精结合的几个因素，显示对结合亲和力值的正贡献，包括分子中存在六元环的特征，分支，电负性值，和极性表面积。
