Large Language Models Transform Organic Synthesis From Reaction Prediction to Automation

TL;DR

Large language models are revolutionizing organic synthesis by enabling automated reaction planning, outcome prediction, and experimental execution, significantly accelerating and democratizing molecular discovery.

Contribution

This paper surveys how LLMs, combined with other AI techniques, are transforming organic chemistry from prediction to automation, highlighting practical applications and future challenges.

Findings

LLMs can propose synthetic routes and predict reaction outcomes.

Coupling LLMs with other AI methods accelerates discovery cycles.

Community initiatives are promoting open benchmarks and explainability.

Abstract

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.