The most common way of understanding SMILES generative models is as grammar-based models that generate SMILES strings that are similar to the training set , akin to language generative models [41]. Alternatively, SMILES generative models can be also understood as action (or policy)-based graph generative models in which a molecular graph is built stepwise. In these models, each step an action is chosen ("add atom", "add bond", etc.) and is sampled from a fixed or varying size action space (or policy) that has all possible actions (even invalid ones) alongside the probability of each happening. A parallelism can be partially drawn for SMILES generative models: the vocabulary is the action space in which atom tokens ("C", "N", "[O-]", etc.) are "add atom" actions, the bond tokens ("=", "#", etc.) are "add bond" actions as are also the ring and branching tokens. The main difference is that "add atom" actions are always adding the new atom to the last atom added, the bond tokens add a bond to an unknown atom, which is specified just after, and the ring and branching tokens add also bonds and enable the model to jump from one place to another. Moreover, a single bond is by default added if no bond is specified between atoms when at least one is aliphatic, and an aromatic bond is added otherwise.