Screening catalysts for the synthesis of cyclic carbonates from CO₂ and epoxides presents a challenge for the research community. Thus, we propose the application of quantitative structure–property relationships (QSPR) modeling and exploratory analysis to assist in the selection of catalysts to produce oleochemical carbonates. QSPR modeling was developed by applying 2D‐descriptors to evaluate the relationship between the molecular structure of organocatalysts and their activity in the production of bio‐based organic carbonates. From the virtual screening, 122 potential catalysts were selected, their catalytic activities were estimated, and the best molecular targets highlighted. Already from the data mining and exploratory analysis, the catalysts' key structural features (e.g. organic structure, molecular arrangement, carbon chain size, and substituent type) were identified. Thus, it was possible to evaluate the similarity between the catalysts and to relate the 2D‐descriptors to their activity. Then, based on QSPR modeling results, cetyltrimethylammonium bromide (CTAB) was proposed as a new catalyst to produce oleochemical carbonates. From the CTAB application, conversions greater than 98% of epoxide were observed in the cycloaddition of CO₂ to epoxidized vegetable oil (rice bran, canola, and soybean). Thus, it was concluded that QSPR modeling and exploratory analysis show potential for screening catalysts for oleochemical carbonate synthesis.

中文翻译：

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定量-性能关系建模和探索性分析在筛选由CO2和生物基环氧化合物合成含油碳酸酯的催化剂中的应用

从CO ₂合成环状碳酸酯的筛选催化剂环氧化物对研究界提出了挑战。因此，我们建议应用定量结构-性质关系（QSPR）建模和探索性分析来协助选择生产油性碳酸盐的催化剂。QSPR建模是通过使用二维描述符来评估有机催化剂的分子结构与其在生物基有机碳酸盐生产中的活性之间的关系而开发的。通过虚拟筛选，选择了122种潜在的催化剂，评估了它们的催化活性，并突出了最佳的分子靶标。通过数据挖掘和探索性分析，已经确定了催化剂的关键结构特征（例如有机结构，分子排列，碳链大小和取代基类型）。从而，可以评估催化剂之间的相似性，并将二维描述符与其活性联系起来。然后，基于QSPR模拟结果，提出了十六烷基三甲基溴化铵（CTAB）作为生产油性碳酸盐的新型催化剂。从CTAB的应用来看，在CO的环加成反应中观察到大于98％的环氧化物转化₂至环氧化植物油（米糠，低芥酸菜子和大豆）。因此，得出的结论是，QSPR建模和探索性分析显示了筛选用于油脂化学碳酸盐合成的催化剂的潜力。