Quantitative Structure-Retention Relationship (QSRR) methodology is a useful tool in chromatography of all kinds, allowing the prediction of analyte retention time and providing insight into the mechanisms of separation. The prediction of retention is useful in reducing method development time and identifying analytes in Non-Targeted Analysis. The varying methods used for geometry optimization, descriptor calculation, feature selection, and model generation in many different QSRR settings are investigated and compared. It is found that the method of geometry optimization and descriptor selection is of less importance than the chromatographic similarity of compounds in the training sets used for model building in order to reduce the error of the model.

定量结构-保留关系（QSRR）方法学是各种色谱分析中的有用工具，可以预测分析物的保留时间并深入了解分离机理。保留时间的预测对于减少方法开发时间和在非目标分析中鉴定分析物很有用。研究和比较了在许多不同的QSRR设置中用于几何优化，描述符计算，特征选择和模型生成的各种方法。已经发现，为了减少模型的误差，几何优化和描述符选择的方法比化合物在用于模型构建的训练集中的色谱相似性的重要性小。