A complex two-stage method was recently developed that allows using all benefits of deep learning for RI prediction using training sets with ~100-200 compounds. The idea of this software is similar to the considered above: deep learning models predict, using a molecule structure, RI for multiple common SP (siloxanes, polyethylene glycol), and then these predicted RI are used as input features for a new model for the given SP and data set. Together with these features (RI for a set of SP), other MD are also used. The difference with the approach considered in this work is that this set of features is fed to a linear support vector regression model (with a non-linear kernel) with predefined parameters without any MD preselection. This software (we call it SVEKLA) allows creating a machine learning model for any SP easily, but these models are not interpretable and use an excessive set of features. RI_PEG_LM is calculated using a linear model. The use of this MD is the most simple and interpretable way to accurately predict RI for IL. A graphical comparison of the accuracy of several different approaches is shown in Fig. .