Synthesis Planning in Reaction Space: A Study on Success, Robustness and Diversity

Computer-aided synthesis planning aims to identify viable synthetic routes from a target compound to readily available building blocks by iteratively decomposing molecules into smaller precursors. Self-play search algorithms, trained with simulated experience, reach state-of-the-art performance. However, these methods typically plan in the molecular rather than the reaction space, leading to redundant or near-duplicate reaction outcomes in the search tree. In this work, we introduce a reaction-centric planning approach that measures the novelty of proposed reactions, thereby constraining the search problem to genuinely unexplored disconnection ideas, i.e., unique ways of decomposing molecules using reactions. Our results show that the overall synthesis planning search space is much smaller than expected due to the absence of diverse disconnection ideas within the underlying retrosynthesis neural network. Surprisingly, we also find that, under a reasonable time budget of less than an hour, online search algorithms outperform state-of-the-art self-play methods and are more robust to environmental changes, such as minor modifications to the available purchasable building blocks.