Synthesis Planning

Synthesis planning, the automated design of chemical synthesis routes, aims to efficiently generate sequences of reactions to produce a target molecule from readily available starting materials. Current research focuses on improving the efficiency and robustness of algorithms, such as bidirectional search and greedy approaches, often incorporating machine learning models, particularly large language models (LLMs) and single-step retrosynthesis predictors, to guide the search process and handle uncertainties in reaction outcomes. These advancements hold significant promise for accelerating drug discovery, materials science, and other chemical research by automating a crucial and time-consuming step in the development of new molecules.