For MolZip-Vec, we combined SMILES strings with numerical descriptors of molecules commonly used in chemoinformatics. Specifically, we utilized a vector comprising 200 molecular descriptors from the RDKit cheminformatics library RDKit (2023), which are typically used to augment graphs in molecular graph representation learning . A complete list of the 200 descriptors can be found in the documentation of the descriptastorus (v2.6.1) Python package. In order to combine and compress the numerical descriptors with the molecular string representation, the values are binned and subsequently translated into a set of non-ASCII Unicode characters. The three molecular string representations (SMILES, DeepSMILES, and SELFIES) used in this work only use ASCII characters, so collisions are avoided. Empirically, we found that 256 is a suitable number of bins. A special character prefixes negative values to represent positive and negative bins distinctly. Each string-based representation of the numerical vector is concatenated to the corresponding SMILES string, significantly improving the RMSE of several datasets listed in Table . In Figure .2, we show that including numerical vectors increasingly improves the performance with growing training set size on the FreeSolv data set . Note that the computational cost for the prediction is slightly higher because of the increased string length.