We follow the method proposed by for the classification tasks and extend it with multiprocess-ing and nearest-neighbour weighing to support imbalanced data sets better. In addition, we implement a framework which provides serialisable text transformations on the input SMILES, including the translation into alternative stringbased molecular representations (DeepSMILES and SELF-IES) and SMILES-based augmentation, which augments a sample by concatenates a user-chosen number of different valid SMILES representations of a given molecule . For both MolZip and MolZip-Vec, we choose the parameter k = 5 for the k-nearest neighbour classification and assume that all data sets are imbalanced, therefore adjusting the kNN classification based on class weights that are calculated using the scikit-learn (v1.3.1) utility function compute class weight. Before benchmarking and comparing transformer-based methods, we evaluated the effect of translation and augmentation transformations. Table .1 compares the performance of SMILES, DeepSMILES, and SELFIES-encoded molecules with otherwise default parameters (k = 5, no augmentation) on various data sets. Based on these results, we decided to use SMILES encoding for our implementation, as it provides a balanced baseline across all evaluated data sets. However, the superior results of the SELFIESand DeepSmiles-encodings on various data sets show that the encoding can indeed have a strong influence on the ob-served performance. Evaluating the effect of augmentation, which concatenates multiple variants of SMILES-encodings of the same molecule (e.g. starting the depth-first search, which constructs the SMILES, at a different atom), using the BBBP and BACE (classification) sets showed mixed results. While the performance of MolZip on the BACE (classification) data set that was later not used in further testing .3) showed the best overall performance for the 11-fold augmentation over the investigated classification and regression data sets, which led us to choose it as the default for MolZip Aug.