Machine learning advancements in organic synthesis: A focused exploration of artificial intelligence applications in chemistry

• Published in: Artificial intelligence chemistry 2024-06, Vol.2 (1), p.100049, Article 100049
• Main Authors: Aal E Ali, Rizvi Syed, Meng, Jiaolong, Khan, Muhammad Ehtisham Ibraheem, Jiang, Xuefeng
• Format: Article
• Language: English
• Subjects: Artificial intelligence; Catalyst design; Chemical selectivity; Material design; Retrosynthesis prediction
• ISSN: 2949-7477; 2949-7477
• DOI: 10.1016/j.aichem.2024.100049

Artificial intelligence (AI) is driving a revolution in chemistry, reshaping the landscape of molecular design. This review explores AI’s pivotal roles in the field of organic synthesis applications. AI accurately predicts reaction outcomes, controls chemical selectivity, simplifies synthesis planning, accelerates catalyst discovery, and fuels material innovation and so on. It seamlessly integrates data-driven algorithms with chemical intuition to redefine molecular design. As AI chemistry advances, it promises accelerated research, sustainability, and innovative solutions to chemistry’s pressing challenges. The fusion of AI and chemistry is poised to shape the field’s future profoundly, offering new horizons in precision and efficiency. This review encapsulates the transformation of AI in chemistry, marking a pivotal moment where algorithms and data converge to revolutionize the world of molecules.