To improve on the existing molecular transformer models, we have developed a novel source-target molecular transformer model, trained at a large scale on 10 11 molecular pairs, that is able to pseudo-exhaustively sample the near-neighborhood, represented by highly precedented transformations and similar target molecules, of a given source molecule. We adopted the same molecular transformer model as proposed by but included a new regularization term into the training process to explicitly control the similarity of the generated molecules. This additional term penalizes the generated target molecules if their similarity to the source molecule does not align with their assigned negative log-likelihood (NLL), which is used as a proxy for precedence (probability). However, as discussed in the methods section, the correlation between similarity and precedence will never be perfect, and there will be similar target molecules associated with low precedented transformations. In contrast to several recent works which used ChEMBL to train transformer models, we used PubChem which contains 40× more molecules, and accordingly many more molecular pairs that can be extracted and used for training. In this study, we retain the similarity principle assumption. Our aim is to develop a versatile model that can be produce similar molecules efficiently and fine-tuned using reinforcement learning for task-specific requirements. Incorporating properties into the training set is also an option but it would require to retrain the model as soon as the desiderata properties change.