RetroSynFormer: Planning multi-step chemical synthesis routes via a Decision Transformer

We present RetroSynFormer, a novel approach to multi-step retrosynthesis planning. Here, we express the task of iteratively breaking down a compound into building blocks as a sequence-modeling problem and train a model based on the Decision Transformer. The synthesis routes are generated by iteratively predicting chemical reactions from a set of predefined rules that encode known transformations, and routes are scored during construction using a novel reward function. RetroSynFormer was trained on routes extracted from the PaRoutes dataset of patented experimental routes. On targets from the PaRoutes test set, the RetroSynFormer could find routes to commercial starting materials for 92% of the targets, and we show that the produced routes on average are close to the reference patented route and of good quality. Furthermore, we explore alternative model implementations and discuss the robustness of the model with respect to beam width, reward function, and template space size. We also compare RetroSynFormer to AiZynthFinder, a conventional retrosynthesis algorithm, and find that our novel model is competitive and complementary to established methodology, thus forming a valuable addition to the field of computer-aided synthesis planning.