The models described up to this point all use SMILES in its most basic configuration (vanilla SMILES). By reducing the molecule to a 1-dimensional string, information is lost about the connectivity between individual atoms. Researchers proposed to leverage information about the graph structure of the molecule. Generally, a molecular graph G is defined by its collection of nodes N (atoms) with features X, edges E (bonds) with features E and connectivity A, which is known as the adjacency matrix (Figure ). Seo et al. augmented the attention mechanism within the Transformer by constructing attention masks through the inclusion of connectivity matrix A. The masks greatly reduce the attention space (parameter space to optimise) of the model, Graph Truncated Attention -GTA, facilitating efficient training. The SMILES token featurisation can be completely replaced with atom (X) and bond (E) featurisation . This was explored by Tu and Coley by coming up with a novel idea to combine the power of graphs with the Transformer (graph Transformers). In their model, Graph2Smiles, the graph object G is fed to a graph encoder that generates a feature vector for each atom.