Another example of such graph Transformer was developed by Wan et al. . Their model, Retroformer, strongly resembles the two-step approach for semi-template models (Figure ). In the first step, the model detects the reaction centre to generate synthons. Synthons are hypothetical molecular units that can be perceived as potential starting reagents. As synthons are chemically invalid, the second step requires the attachment of atoms or leaving groups to the synthons, generating feasible starting materials. Retroformer leverages the graph Transformer to first identify the reaction centre. Instead of generating synthons (as in Figure ), the reaction region is introduced as token masks in the second Transformer model, translating the product SMILES to reactant SMILES. The utilisation of attention masks follows the same idea as GTA, that is to shrink the attention space. Whilst the frameworks mentioned in this section differ in the encoding strategy (i.e. SMILES or molecular graphs), they all generate a SMILES sequence as their output. Presently, researchers have attempted to find alternatives to SMILES for retrosynthesis. This interesting area of research is however beyond the scope of this investigation.