Chemical Synthesis

Chemical synthesis research focuses on automating and optimizing the design and execution of chemical reactions, aiming to accelerate drug discovery, materials science, and other fields. Current efforts leverage machine learning, particularly large language models (LLMs) and graph neural networks (GNNs), to predict reaction conditions, design molecules, and plan retrosynthetic routes, often incorporating multimodal data (text, graphs, SMILES strings). These advancements improve efficiency and accuracy in synthesis planning, reducing reliance on trial-and-error and enabling the exploration of a wider chemical space. The resulting tools promise to significantly enhance productivity and accelerate scientific discovery in chemistry and related disciplines.