The discovery of novel organic molecules to fight and treat diseases is a major challenge in the pharmaceutical domain. As the number of potential drugs is increasing exponentially, computational techniques such as predictive modelling and reaction optimisation are in high demand. Nevertheless, the journey from discovering a drug to its production at a large scale is long and costly. Traditionally, synthesis pathways are discovered by identifying single reaction steps -in a backwards fashion -until suitable precursors are found. This approach is formally known as retrosynthesis . The curation of synthesis routes through retrosynthesis generally depends on the experience and preference of the chemist. Due to the vast size of the chemical space, manual synthesis planning is time-intensive, challenging, and mostly suboptimal . Therefore, researchers have focused on developing computational tools that can aid the selection process . In fact, it was Corey et al. in 1972 that formulated the idea of encoding reaction-and selectivity rules and heuristics into a machinereadable format to perform retrosynthesis in an automated fashion. His pioneering work on computer-aided synthesis planning (CASP) gave rise to the development of well-known retrosynthesis systems such as Merck's SYNTHIA (formerly Chematica) or SYNLMA . These algorithms are known as rule-based expert systems since they rely on prior knowledge provided by a human. Unfortunately, expert systems exhibit limitations in terms of their confined scope and scalability concerning novel molecules and reaction types . To overcome the limitations associated with rule-based systems, Segler and Waller proposed to learn directly from the reaction data by leveraging machine learning (ML). Without relying on pre-defined selectivity rules and heuristics, their model identified the "best" reaction rule (template) by learning directly from the data. Their approach demonstrated a significant improvement over existing rule-based systems on a small case study of 103 rules . Since their publication in 2017, more than 30 ML-based (single-step ) retrosynthesis frameworks have been developed. In spite of these numbers, there are still various aspects that require attention before the frameworks can be considered fully functional. In their review, Coley et al. described the four fundamental building blocks of retrosynthesis, which collectively form a comprehensive framework: i) An algorithm that decomposes a molecular target into reactants, known as single-step retrosynthesis. ii) A database encompassing building blocks that act as starting materials of the synthesis route. iii) A multi-step algorithm , that makes multiple calls to a single-step model to construct several synthesis routes in a tree fashion. iv) A scoring metric (e.g. molecular accessibility ), which guides the multi-step synthesis planning . Whilst building blocks (ii-iv) ensure the construction of synthesis routes, it is the single-step model (i) that defines the critical reaction chemistry.