In cheminformatics, graph-based methods use graph representations to show molecule structures and interactions, offering a flexible means of understanding and predicting chemical properties and behaviour ( ). These techniques use graph representations to explain molecules using atoms as nodes and bonds as edges. This allows for relational as well as structural elements. This approach is significant in drug development since it can effectively control complex chemical structures and their interactions, surpassing more traditional techniques. Graph theory and algorithms let researchers rapidly examine molecular fingerprints, predict biological activity, and optimise lead compounds ( ). Therefore, graph-based methods are crucial for the advancement of computational chemistry as well as for the quick identification of new therapeutic compounds (Figure . Representation learning techniques capture intricate traits and patterns, helping nodes and edges in a graph undergo metamorphosis. Comparable measurements evaluate the degree of similarity between nodes or subgraphs, therefore facilitating the identification of objects with comparable architectures. By grouping nodes with like characteristics, clustering methods help to detect communities and trends. Crucially for identifying significant nodes or paths in biological networks, centrality and pathfinding algorithms assess the value of nodes and select the optimal paths within the graph. These graph-based approaches in many disciplines, including drug development and protein interaction studies, help researchers find latent insights and make well-informed decisions jointly ( ). molecular fingerprinting, molecular similarity assessment, and predictive modelling of biological activities ( ). These methods leverage graph representations to capture intricate structural details and relational data within molecular structures, offering powerful tools for drug discovery and computational chemistry. Here are examples of graph embedding methods ( ) based on knowledge graphs that boost DTI prediction performance with the help of ML or DL-based models constructed on low-dimensional feature representation. The graph-based method uses correlations between correlation drug-drug, target-target, and similar matrices, such as DASPfind ( ). DASPfind orders correlations based on their path scores to determine the top 1%. The DASPfind approach is superior to most network-based models ( ). Also, DTINET ( ) uses graph embedding approaches and matrix factorisation to foresee novel DTIs from a heterogeneous graph.