Department of Colloid and Lipid Science, Faculty of Chemistry, Gdańsk University of Technology, Narutowicza St. 11/12, 80-233 Gdańsk, Poland.
Department of Hydraulic Engineering, Faculty of Civil and Environmental Engineering, Gdańsk University of Technology, Narutowicza St. 11/12, 80-233 Gdańsk, Poland.
Molecules. 2021 Jan 20;26(3):534. doi: 10.3390/molecules26030534.
The efficiency of micellar solubilization is dictated inter alia by the properties of the solubilizate, the type of surfactant, and environmental conditions of the process. We, therefore, hypothesized that using the descriptors of the aforementioned features we can predict the solubilization efficiency, expressed as molar solubilization ratio (MSR). In other words, we aimed at creating a model to find the optimal surfactant and environmental conditions in order to solubilize the substance of interest (oil, drug, etc.). We focused specifically on the solubilization in biosurfactant solutions. We collected data from literature covering the last 38 years and supplemented them with our experimental data for different biosurfactant preparations. Evolutionary algorithm (EA) and kernel support vector machines (KSVM) were used to create predictive relationships. The descriptors of biosurfactant (logP, measure of purity), solubilizate (logP, molecular volume), and descriptors of conditions of the measurement (T and pH) were used for modelling. We have shown that the MSR can be successfully predicted using EAs, with a mean R of 0.773 ± 0.052. The parameters influencing the solubilization efficiency were ranked upon their significance. This represents the first attempt in literature to predict the MSR with the MSR calculator delivered as a result of our research.
胶束增溶的效率主要取决于增溶物的性质、表面活性剂的类型和过程的环境条件。因此，我们假设可以使用上述特征的描述符来预测增溶效率，用摩尔增溶比 (MSR) 表示。换句话说，我们旨在创建一个模型，以找到最佳的表面活性剂和环境条件，以增溶感兴趣的物质（油、药物等）。我们特别关注生物表面活性剂溶液中的增溶。我们从涵盖过去 38 年的文献中收集数据，并补充了我们对不同生物表面活性剂制剂的实验数据。我们使用进化算法 (EA) 和核支持向量机 (KSVM) 来创建预测关系。生物表面活性剂的描述符（logP、纯度测量值）、增溶物的描述符（logP、分子体积）和测量条件的描述符（T 和 pH）用于建模。我们已经表明，使用 EA 可以成功预测 MSR，平均 R 为 0.773 ± 0.052。根据其重要性对影响增溶效率的参数进行了排序。这是文献中首次尝试使用我们研究的结果作为 MSR 计算器来预测 MSR。
