We use both linear regression and a Gradient Boosted tree method (LightGBM) in this study. For the linear regression, a molecule is represented by positional seven-hot encoding, i.e. vector with 287 (41 × 7) binary elements, where each chunk of 41 is one-hot encoded representation of a non-H substituent at a particular position. This representation was used to train three different machine learning (ML)-models using SQM data. Linear regression and kernel ridge regression as implemented in Scikit-learn and a gradient boosted tree method as implemented in LightGBM. We found very little difference in performance between regression and kernel ridge regression and use the former, simpler, model in this study.