The development of flotation collectors is usually time-consuming and costly. Quantitative structure–activity relationship (QSAR) is one of the most powerful methods for designing and modifying molecules. Introducing QSAR into the development of collectors can significantly compensate for the above shortcomings. The molecular structure, conceptual density functional theory (CDFT) and electrostatic potential (ESP) are chosen as descriptors of xanthate in this article. Pearson correlation coefficient is used to filter descriptors to prevent overfitting, and a QSAR model is established for xanthate. The results of the model show that for the common xanthates, hydrophobicity and dipole moment can describe the relationship between their structure and flotation performance (R2 = 0.9647). For the amide xanthates, the hydrophobicity, dipole moment, and non-polar region surface area can describe the relationship between their structures and flotation performance (R2 = 0.9478). The accuracy of the model was verified using the test set, and the predicted flotation index (FI) values of sodium isobutyl xanthate (SIBX), potassium O-(6-(hexylamino)-6-oxohexyl) xanthate (PHAHX), potassium O-(6-(butylamino)-6-oxohexyl) xanthate (PBAHX) and potassium O-(4-(butylamino)-4-oxobutyl) xanthate (PBABX) were 0.7099, 0.8614, 0.8061, and 0.7631, respectively. And the relative errors with the experimental values were only 7.89 %, 1.85 %, 4.00 %, and 2.57 %, respectively, demonstrating the reliability of the established model. The QSAR model provides a theoretical basis for the subsequent design of collectors with better flotation performance and predicts the collecting capacities of collectors for chalcopyrite and pyrite.

中文翻译：

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硫化铜捕收剂分子设计和浮选预测的 QSAR 研究


浮选除尘器的开发通常既耗时又昂贵。定量构效关系 （QSAR） 是设计和修饰分子的最强大方法之一。将 QSAR 引入到收集器的开发中，可以显着弥补上述不足。本文选择分子结构、概念密度泛函理论 （CDFT） 和静电势 （ESP） 作为黄药的描述符。使用 Pearson 相关系数过滤描述符以防止过拟合，并为黄药建立了 QSAR 模型。模型结果表明，对于常见的黄原酸盐，疏水性和偶极矩可以描述它们的结构与浮选性能之间的关系 （R2 = 0.9647）。对于酰胺黄原酸盐，疏水性、偶极矩和非极性区域表面积可以描述它们的结构与浮选性能之间的关系 （R2 = 0.9478）。使用测试集验证了模型的准确性，异丁基黄原酸钠 （SIBX）、O-（6-（己氨基）-6-氧代氧基） 黄原酸钾 （PHAHX）、O-（6-（丁氨基）-6-氧代氧基） 黄原酸钾 （PBAHX） 和O-（4-（丁氨基）-4-氧代丁基）黄原酸钾 （PBABX） 的预测浮选指数 （FI） 值分别为 0.7099、0.8614、0.8061 和 0.7631。与实验值的相对误差分别为7.89 %、1.85 %、4.00 %和2.57 %，证明了所建立模型的可靠性。QSAR 模型为后续设计具有更好浮选性能的捕收剂提供了理论依据，并预测了黄铜矿和黄铁矿捕收剂的捕收能力。