From this investigation, the following research directions are proposed: The template-based models demonstrate the best performance but struggle with novel chemistry, and their performance is known to degrade with larger datasets. To address this challenge, we suggest a hybrid approach, integrating Graph Neural Networks to identify reaction centres prior to the template classification task to reduce the number of applicable templates for a given molecule. Template-free models (Transformer) using SMILES are seen to underperform due to the complex many-to-many mapping problem between SMILES. This prompts the consideration of alternative string-based representations such as SELFIES . SELFIES are robust in nature as each SELFIES can be translated to a valid molecule directly. Nevertheless, as SELFIES are non-unique, the many-to-many mapping problem remains. Another ongoing challenge for template-free models lies in the prediction of diverse reactions. As shown by the TiedTransformer model, diverse predictions can result in a large proportion of chemically feasible predictions whilst providing the end-user with more choices. Therefore, using latent variables or tags to increase the diversity of reaction prediction is an intriguing idea. Finally, semi-template (graph-edit) models show a basic understanding of chemical knowledge but struggle in the determination of feasible reaction centres for more complex molecules or reaction mechanisms. Incorporating chemically informed features, such as electronegativity, bond strength and dissociation energy, is suggested for performance improvement. The difficulty in predicting changes in stereochemistry during reactions is an ongoing challenge, with attempts made to address this issue by Zhong et al. . Overall, we emphasise the need for advancements in molecular representation as input featurisation to the model and diversity in retrosynthesis prediction.