" Integration of Artificial Intelligence in Organic Chemistry: Recent Advances, Applications, and Challenges"

The integration of Artificial Intelligence (AI) into organic chemistry has emerged as a transformative approach, enabling unprecedented accuracy, efficiency, and speed in both research and industrial domains. From predictive modeling of complex organic reactions to retrosynthetic planning and high-throughput screening, AI techniques—particularly deep learning and graph neural networks—are reshaping the discovery and optimization of molecules in fields such as pharmaceuticals, petrochemicals, and materials science. This paper provides a comprehensive review of recent advances in AI-driven organic chemistry, focusing on industrial applications. It also presents original analyses derived from publicly available reaction datasets and molecular libraries, revealing the potential of custom-trained models in optimizing synthetic routes. The paper concludes with a discussion on current challenges, including data quality, model interpretability, and industrial scalability, while outlining future research directions for hybrid intelligent systems and autonomous chemical laboratories.The integration of Artificial Intelligence (AI) into organic chemistry has emerged as a transformative approach, enabling unprecedented accuracy, efficiency, and speed in both research and industrial domains. From predictive modeling of complex organic reactions to retrosynthetic planning and high-throughput screening, AI techniques—particularly deep learning and graph neural networks—are reshaping the discovery and optimization of molecules in fields such as pharmaceuticals, petrochemicals, and materials science. This paper provides a comprehensive review of recent advances in AI-driven organic chemistry, focusing on industrial applications. It also presents original analyses derived from publicly available reaction datasets and molecular libraries, revealing the potential of custom-trained models in optimizing synthetic routes. The paper concludes with a discussion on current challenges, including data quality, model interpretability, and industrial scalability, while outlining future research directions for hybrid intelligent systems and autonomous chemical laboratories.

کلیدواژه ها:

Artificial Intelligence ، Organic Chemistry