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説明:

Progress in chemistry has been driven by the streamlining of inverse problem-solving methods. In the history of chemistry, several revolutionary technologies have led to leaps forward: the establishment of atomistic theory in the 19th century, structural analysis by spectroscopy in the 20th century, and the development of simulation by theoretical chemistry. Currently, chemistry is about to make a significant leap forward by integrating generative artificial intelligence (AI). In 2016, deep learning techniques were introduced in this domain, leading to explosive development. This paper reviews the development path, including traditional models such as variational autoencoders and more up-to-date models such as large language models and diffusion models. We also discuss how AI can have a real impact on chemistry, including the possibilities and problems associated with synthesizing AI-generated molecules.

権利情報:

• Creative Commons BY-NC-ND Attribution-NonCommercial-NoDerivs 4.0 International
  

キーワード: Molecular Design, AI

刊行年月日: 2026-03-11

出版者: American Chemical Society (ACS)

掲載誌:

• Chemical Reviews (ISSN: 00092665) vol. 126 issue. 5 p. 3007-3054

研究助成金:

• Ministry of Education, Culture, Sports, Science and Technology JPMXP1020230120
• Exploratory Research for Advanced Technology JPMJER1903
• Core Research for Evolutional Science and Technology JPMJCR21O2
• Precursory Research for Embryonic Science and Technology JPMJPR24T8
• Japan Society for the Promotion of Science JP20H04251
• Japan Society for the Promotion of Science JP20H05963
• Japan Society for the Promotion of Science JP24H00473
• Japan Science and Technology Agency JPMJBY24F0
• Fusion Oriented REsearch for disruptive Science and Technology JPMJFR232U

原稿種別: 出版者版 (Version of record)

MDR DOI:

公開URL: https://doi.org/10.1021/acs.chemrev.5c00689

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更新時刻: 2026-09-16 10:38:41 +0900

MDRでの公開時刻: 2026-09-16 12:27:18 +0900