Methods that predict chromatographic retention using the analyte structure as an input are usually referred to as quantitative structure-retention relationships (QSRR) . One of the application areas of this method is the non-target GC-MS analysis using a mass spectral library search for rejection of false candidates. QSRR can be considered as a method that provides an insight into chromatographic separation . When predicting a retention index (RI) based on some molecular descriptors (i.e., numerical values that characterize the structure of a molecule), the contribution of particular molecular descriptors (MD) and a set of selected MD can provide valuable information about the nature of separation, and the model is considered as an interpretable one . Almost all work on QSRR for GC is limited to the most typical and well-characterized polymeric SP. In liquid chromatography conditions, more factors influence retention and the use of QSRR to study the separation mechanism is even more common . QSSR are also used as a convenient task in order to develop and demonstrate chemometric, statistical, and machine learning methods.