1. AI for organic and polymer synthesis.
- Author
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Hong, Xin, Yang, Qi, Liao, Kuangbiao, Pei, Jianfeng, Chen, Mao, Mo, Fanyang, Lu, Hua, Zhang, Wen-Bin, Zhou, Haisen, Chen, Jiaxiao, Su, Lebin, Zhang, Shuo-Qing, Liu, Siyuan, Huang, Xu, Sun, Yi-Zhou, Wang, Yuxiang, Zhang, Zexi, Yu, Zhunzhun, Luo, Sanzhong, and Fu, Xue-Feng
- Abstract
Recent years have witnessed the transformative impact from the integration of artificial intelligence with organic and polymer synthesis. This synergy offers innovative and intelligent solutions to a range of classic problems in synthetic chemistry. These exciting advancements include the prediction of molecular property, multi-step retrosynthetic pathway planning, elucidation of the structure-performance relationship of single-step transformation, establishment of the quantitative linkage between polymer structures and their functions, design and optimization of polymerization process, prediction of the structure and sequence of biological macromolecules, as well as automated and intelligent synthesis platforms. Chemists can now explore synthetic chemistry with unprecedented precision and efficiency, creating novel reactions, catalysts, and polymer materials under the data-driven paradigm. Despite these thrilling developments, the field of artificial intelligence (AI) synthetic chemistry is still in its infancy, facing challenges and limitations in terms of data openness, model interpretability, as well as software and hardware support. This review aims to provide an overview of the current progress, key challenges, and future development suggestions in the interdisciplinary field between AI and synthetic chemistry. It is hoped that this overview will offer readers a comprehensive understanding of this emerging field, inspiring and promoting further scientific research and development. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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