The predicted based on the molecule structure RI for PEG was used as the MD for further prediction of RI for IL-based SP (see Fig. ). So, this is a supplementary task for this work. We used two methods for prediction of RI for PEG. The first one is the use of a quite accurate deep learning model, previously described in our previous works . In this case, a multimodal ensemble of two deep neural networks was used. The neural networks were trained using the NIST 17 database. The models use SMILES string representations of models, various MD, and molecular fingerprints used as an input representation of molecules. The models were described in the previous work and used in the unchanged form. The newly developed and described in this work CHERESHNYA software calls our previous software for prediction of RI for the DB-WAX column. This predicted RI value is further referred as the RI_PEG_DL descriptor. The second approach is a linear model for prediction of RI on PEG. The following set of features was used: 243 2D MD and 84 functional groups counters generated using the Chemistry Development Kit, version 2.7.1 (CDK) ; 208 2D MD of various types and 42 MQN (so-called Molecular Quantum Numbers ) generated using the RDKit library, version 2023.09.4; 4860 Klekota-Roth substructure counters (Klekota-Roth counting fingerprint ). The first subset of features (functional groups counters and CDK descriptors) was the same as used in our previous work . All MD were scaled to the range [0; 1]: