Others have been treating molecular optimization as a translation task between a source and a target molecule, inspired by natural language processing (NLP). These source-target based methods require a data set of molecular pairs for training. Inspired by the medicinal chemistry concept of matched molecular pairs, He et al. generated molecular pairs based on MMP, identical scaffolds, or a Tanimoto similarity above a certain threshold. The set of pairs were used to train different molecular transformer models that was used to explore the region of the chemical space relatively close to a source compound.