Integrating Artificial Intelligence into Chemical Synthesis: Advances, Applications, and Challenges

Abstract Artificial Intelligence (AI) is redefining the landscape of chemical synthesis by introducing data-driven models for retrosynthetic planning, reaction prediction, optimization, and automated experimentation. With recent advancements in machine learning (ML), graph neural networks (GNNs), large language models (LLMs), and self-driving laboratories (SDLs), chemical discovery is becoming faster, greener, and more reproducible. This paper reviews current progress (2023–2026) in AI-assisted synthesis, highlighting recent breakthroughs, case studies, and challenges of integrating computational intelligence with experimental chemistry.