Synthetic Route

Synthetic route design, the process of determining the optimal chemical pathway to synthesize a target molecule, is a crucial challenge in chemistry and materials science. Current research focuses on developing machine learning models, including generative flow networks, transformers, and large language models, to predict both single-step and multi-step retrosynthetic pathways, often incorporating graph-based representations of molecules and leveraging techniques like reinforcement learning and in-context learning to improve accuracy and efficiency. These advancements aim to accelerate drug discovery and materials development by automating and optimizing the design of synthetic routes, potentially reducing costs and time associated with experimental synthesis. The field is also exploring methods to ensure the synthesizability of predicted molecules and to incorporate criteria such as cost and yield into the design process.