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

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 planni...

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Bibliographic Details
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
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Summary: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.
ISSN:2949-7477
2949-7477
DOI:10.1016/j.aichem.2024.100049