Large Language Models Transform Organic Synthesis From Reaction Prediction to Automation
This addresses the challenge for chemists in planning and running reactions more efficiently, though it is incremental as it builds on existing AI and automation techniques.
The paper tackles the problem of improving organic synthesis by using large language models (LLMs) to predict reactions and automate experiments, resulting in faster discovery cycles and greener chemistry.
Large language models (LLMs) are beginning to reshape how chemists plan and run reactions in organic synthesis. Trained on millions of reported transformations, these text-based models can propose synthetic routes, forecast reaction outcomes and even instruct robots that execute experiments without human supervision. Here we survey the milestones that turned LLMs from speculative tools into practical lab partners. We show how coupling LLMs with graph neural networks, quantum calculations and real-time spectroscopy shrinks discovery cycles and supports greener, data-driven chemistry. We discuss limitations, including biased datasets, opaque reasoning and the need for safety gates that prevent unintentional hazards. Finally, we outline community initiatives open benchmarks, federated learning and explainable interfaces that aim to democratize access while keeping humans firmly in control. These advances chart a path towards rapid, reliable and inclusive molecular innovation powered by artificial intelligence and automation.