Literature DB >> 34264069

Artificial Intelligence in Chemistry: Current Trends and Future Directions.

Zachary J Baum1, Xiang Yu1, Philippe Y Ayala1, Yanan Zhao1, Steven P Watkins1, Qiongqiong Zhou1.   

Abstract

The application of artificial intelligence (AI) to chemistry has grown tremendously in recent years. In this Review, we studied the growth and distribution of AI-related chemistry publications in the last two decades using the CAS Content Collection. The volume of both journal and patent publications have increased dramatically, especially since 2015. Study of the distribution of publications over various chemistry research areas revealed that analytical chemistry and biochemistry are integrating AI to the greatest extent and with the highest growth rates. We also investigated trends in interdisciplinary research and identified frequently occurring combinations of research areas in publications. Furthermore, topic analyses were conducted for journal and patent publications to illustrate emerging associations of AI with certain chemistry research topics. Notable publications in various chemistry disciplines were then evaluated and presented to highlight emerging use cases. Finally, the occurrence of different classes of substances and their roles in AI-related chemistry research were quantified, further detailing the popularity of AI adoption in the life sciences and analytical chemistry. In summary, this Review offers a broad overview of how AI has progressed in various fields of chemistry and aims to provide an understanding of its future directions.

Keywords:  CAS Content Collection; analytical chemistry; artificial intelligence; biochemistry

Year:  2021        PMID: 34264069     DOI: 10.1021/acs.jcim.1c00619

Source DB:  PubMed          Journal:  J Chem Inf Model        ISSN: 1549-9596            Impact factor:   4.956


  4 in total

1.  Working at the interfaces of data science and synthetic electrochemistry.

Authors:  Jesus I Martinez Alvarado; Jonathan M Meinhardt; Song Lin
Journal:  Tetrahedron Chem       Date:  2022-03-26

2.  Feasibility and application of machine learning enabled fast screening of poly-beta-amino-esters for cartilage therapies.

Authors:  Stefano Perni; Polina Prokopovich
Journal:  Sci Rep       Date:  2022-08-20       Impact factor: 4.996

3.  Diving into the Deep End: Machine Learning for the Chemist.

Authors:  Silvia Imberti
Journal:  ACS Omega       Date:  2022-07-20

4.  Virtual data augmentation method for reaction prediction.

Authors:  Xinyi Wu; Yun Zhang; Jiahui Yu; Chengyun Zhang; Haoran Qiao; Yejian Wu; Xinqiao Wang; Zhipeng Wu; Hongliang Duan
Journal:  Sci Rep       Date:  2022-10-12       Impact factor: 4.996

  4 in total

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