Until now, more traditional methods, such as random forests, support vector machines, etc., have been commonly used to predict molecular properties. In contrast to GNNs, these traditional approaches often operate on precomputed molecular features such as molecular weight, polarity, charge, number of carbon atoms, etc. Although these molecular features prove to be good predictors for various molecular properties, it is hypothesized that operating on these more \"raw\", \"low-level\", features could prove even better.