Quantitative Structure-Retention Relationships (QSRR) methodology combined with the Hydrophobic Subtraction Model (HSM) have been utilized to accurately predict retention times for a selection of analytes on several different reversed phase liquid chromatography (RPLC) columns. This approach is designed to facilitate early prediction of co-elution of analytes, for example in pharmaceutical drug discovery applications where it is advantageous to predict whether impurities might be co-eluted with the active drug component. The QSRR model utilized VolSurf+ descriptors and a Partial Least Squares regression combined with a Genetic Algorithm (GA-PLS) to predict the solute coefficients in the HSM. It was found that only the hydrophobicity (η' H) term in the HSM was required to give the accuracy necessary to predict potential co-elution of analytes. Global QSRR models derived from all 148 compounds in the dataset were compared to QSRR models derived using a range of local modelling techniques based on clustering of compounds in the dataset by the structural similarity of compounds (as represented by the Tanimoto similarity index), physico-chemical similarity of compounds (represented by log D), the neutral, acidic, or basic nature of the compound, and the second dominant interaction between analyte and stationary phase after hydrophobicity. The global model showed reasonable prediction accuracy for retention time with errors of 30 s and less for up to 50% of modeled compounds. The local models for Tanimoto, nature of the compound and second dominant interaction approaches all exhibited prediction errors less than 30 s in retention time for nearly 70% of compounds for which models could be derived. Predicted retention times of five representative compounds on nine reversed-phase columns were compared with known experimental retention data for these columns and this comparison showed that the accuracy of the proposed modelling approach is sufficient to reliably predict the retention times of analytes based only on their chemical structures.

定量结构-保留关系（QSRR）方法与疏水减法模型（HSM）相结合已被用于准确预测在几种不同的反相液相色谱（RPLC）色谱柱上选择的分析物的保留时间。该方法旨在促进分析物共洗脱的早期预测，例如在药物发现应用中，在该应用中，预测杂质是否可能与活性药物成分共洗脱是有利的。QSRR模型利用VolSurf +描述符和偏最小二乘回归结合遗传算法（GA-PLS）来预测HSM中的溶质系数。结果发现，仅疏水性（η” ħ需要使用HSM中的）一词来提供预测分析物潜在共洗脱所需的准确度。将数据集中所有148种化合物衍生的全局QSRR模型与使用一系列局部建模技术得出的QSRR模型进行比较，这些模型基于数据在化合物中的化合物聚类，包括化合物的结构相似性（以Tanimoto相似性指数表示），化合物的化学相似性（用对数D表示），化合物的中性，酸性或碱性，以及疏水性之后分析物与固定相之间的第二主要相互作用。全局模型对保留时间显示出合理的预测准确性，对于最多50％的建模化合物，误差为30 s和更少。Tanimoto的本地模型，化合物的性质和第二种主要相互作用方法都对将近70％的可推导模型的化合物的保留时间均显示了小于30 s的预测误差。将五个代表性化合物在九个反相色谱柱上的预计保留时间与这些色谱柱的已知实验保留数据进行了比较，该比较表明，所提出的建模方法的准确性足以可靠地仅根据分析物的化学性质预测分析物的保留时间。结构。