What question did this study set out to answer?

To explore how artificial intelligence is transforming chemical synthesis through data-driven approaches.

April 10, 2026Open Access

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

Key Points

To explore how artificial intelligence is transforming chemical synthesis through data-driven approaches.
Review of recent literature from 2023 to 2026
Analysis of advancements in machine learning and graph neural networks
Discussion of case studies in automated experimentation
Identified significant breakthroughs in AI-assisted chemical synthesis
Showed improvements in speed and reproducibility of chemical discoveries
Discussed challenges in integrating AI with experimental chemistry

Abstract

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.

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

Key Points

Abstract

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