Retention prediction for unknown compounds based on Quantitative Structure-Retention Relationships (QSRR) can lead to rapid “scoping” method development in chromatography by simplifying the selection of chromatographic parameters. The use of retention factor ratio (or k-ratio) as a chromatographic similarity index can be a potent method to cluster similar compounds into a training set to generate an accurate predictive QSRR model provided that its limitation – that the method is impractical for retention prediction for unknown compounds – is successfully addressed. In this work, we propose a localised QSRR modelling approach with the aim of compensating the critical limitation in the otherwise successful k-ratio filter-based QSRR modelling. The approach is to combine a k-ratio filter with both Tanimoto similarity (TS) and a ΔlogP index (i.e., logP-Dual filter). QSRR models for two retention parameters (a and b) in the linear solvent strength (LSS) model in ion chromatography (IC), logk = a − blog[eluent], were generated for larger organic cations (molecular mass up to 506) on a Thermo Fisher Scientific CS17 column. The application of the developed logP-Dual filter resulted in the production of successful QSRR models for 50 organic cations out of 87 in the dataset. The predicted a- and b-values of the models were then applied to the LSS model to predict the corresponding retention times. External validation showed that QSRR models for a-, b- and t_R- values with excellent accuracy and predictability (Q_ext(F2)² of 0.96, 0.95, and 0.96, RMSEP of 0.06, 0.02, and 0.38 min) were created successfully, and these models can be employed to speed up the “scoping” phase of method development in IC.

基于定量结构-保留关系（QSRR）的未知化合物的保留预测可以通过简化色谱参数的选择，在色谱中快速开发“作用域”方法。使用保留因子比率（或k比率）作为色谱相似性指数可能是将相似化合物聚类到训练集中以生成准确的预测QSRR模型的有效方法，条件是其局限性–该方法对于保留预测不切实际适用于未知化合物–已成功解决。在这项工作中，我们提出了一种本地化的QSRR建模方法，旨在补偿否则成功的基于k比率滤波器的QSRR建模中的关键限制。方法是合并一个k与两个谷本相似性（TS）和一个Δlog-ratio滤波器P指数（即，登录P -双过滤器）。对于较大的有机阳离子（分子量最大为506），生成了离子色谱（IC）中线性溶剂强度（LSS）模型中两个保留参数（a和b）的QSRR模型，log k  =  a - b log [洗脱液] ）在Thermo Fisher Scientific CS17色谱柱上。开发的log P -Dual过滤器的应用导致针对数据集中的87个中的50个有机阳离子成功生成了QSRR模型。预测的-和b然后将模型的-值应用于LSS模型，以预测相应的保留时间。外部验证表明，成功创建了具有极好的准确性和可预测性（Q _ext（F2） ²为0.96、0.95和0.96，RMSEP为0.06、0.02和0.38分钟）的a-，b-和t _R-值的QSRR模型。，这些模型可用于加快IC方法开发的“范围界定”阶段。