Department of Chemistry, National University of Singapore, Singapore 117543, Singapore.
Department of Chemical Engineering, Pukyong National University, Busan 48513, Korea.
Molecules. 2020 Jul 6;25(13):3085. doi: 10.3390/molecules25133085.
Prediction of the retention time from the molecular structure using quantitative structure-retention relationships is a powerful tool for the development of methods in reversed-phase HPLC. However, its fundamental limitation lies in the fact that low error in the prediction of the retention time does not necessarily guarantee a prediction of the elution order. Here, we propose a new method for the prediction of the elution order from quantitative structure-retention relationships using multi-objective optimization. Two case studies were evaluated: (i) separation of organic molecules in a Supelcosil LC-18 column, and (ii) separation of peptides in seven columns under varying conditions. Results have shown that, when compared to predictions based on the conventional model, the relative root mean square error of the elution order decreases by 48.84%, while the relative root mean square error of the retention time increases by 4.22% on average across both case studies. The predictive ability in terms of both retention time and elution order and the corresponding applicability domains were defined. The models were deemed stable and robust with few to no structural outliers.
使用定量结构-保留关系预测保留时间是开发反相高效液相色谱法的有力工具。然而，其基本局限性在于，保留时间预测的低误差并不一定能保证洗脱顺序的预测。在这里，我们提出了一种使用多目标优化预测定量结构-保留关系中洗脱顺序的新方法。评估了两个案例研究：（i）在 Supelcosil LC-18 柱中分离有机分子，和（ii）在七种不同条件下的柱中分离肽。结果表明，与基于传统模型的预测相比，洗脱顺序的相对均方根误差平均降低了 48.84%，而保留时间的相对均方根误差平均增加了 4.22%。在这两个案例研究中，都定义了保留时间和洗脱顺序的预测能力及其相应的适用域。这些模型被认为是稳定和鲁棒的，几乎没有结构异常值。
