The synthesis of chemical matter is often viewed as a rate-limiting step in material sciences, crop protection and drug discovery . Crafting complex molecules typically involves multi-step syntheses, encompassing various reaction steps, each presenting multi-parameter optimization challenges . This high complexity makes chemical reactions time-and resource-intensive . Exploiting the growing volume of chemical reaction data could enhance synthesis planning and potentially boost success rates . In recent years, machine learning has shown applications to a broad variety of challenges in chemistry . Graph neural networks, transformers, and recurrent neural networks have proven effective in reaction prediction and synthesis planning tasks .