Retrosynthesis Prediction

Retrosynthesis prediction, the computational task of designing chemical synthesis routes, aims to identify precursor molecules for a given target molecule, accelerating drug discovery and materials science. Current research heavily utilizes large language models (LLMs), graph neural networks (GNNs), and transformers, often combined with search algorithms like A* or Monte Carlo Tree Search, to predict single-step and multi-step reaction pathways. Focus areas include improving model accuracy, particularly for complex molecules and rare reaction types, enhancing the efficiency of inference, and addressing the challenge of predicting chemically feasible reactions. These advancements promise to significantly streamline the synthesis planning process, reducing time and cost in chemical research and development.