Precedence of transformations is determined by the distribution of the observed data (empirical distribution) and, more specifically, by a criterion of pair generation. Given a source molecule, we consider the space consisting of all the possible transformations that result into not violation the criterion used for generating pairs. We define as precedence the empirical data distribution on this space. In our approach, we introduce an inductive bias directing our model away from the empirical data distribution created by pair generation. This is achieved by explicitly adjusting sampling probability through regularization (see Equation ) of negative log-likelihoods (NLLs) to align with a similarity metric rather than solely adhering to the empirical data distribution. We consider auto-regressive transformers with SMILES, which have emerged as powerful paradigm for molecular generation . In this approach, each generated token in the SMILES string is added with a certain probability. Thus each generated molecule (SMILES string) will have a certain probability. A precedented transformation for a given target molecule are those that have high probability to be applied to a given source molecule. The precedence associated to the molecular transformation to a generated target molecule is learnt by the transformer model during the training phase, but the precedence is not necessarily related to the molecular similarity between the source and target molecule, i.e. a transformer model can generate target molecules with a high precedence which are very dissimilar to the source molecule. This behaviour is not optimal in applications such as lead optimization where one would like to be able to generate all similar and relevant compounds given a specific source molecule.