Citation: Run Yang,  Huajie Pang,  Huiping Zang,  Ruizhong Zhang,  Zhicheng Zhang,  Xiyan Li,  Libing Zhang. Artificial Intelligence-Enabled DNA Computing: Exploring New Frontiers in Bioinformatics[J]. University Chemistry, ;2025, 40(9): 107-117. doi: 10.12461/PKU.DXHX202412135 shu

Artificial Intelligence-Enabled DNA Computing: Exploring New Frontiers in Bioinformatics

  • Corresponding author: Zhicheng Zhang,  Xiyan Li,  Libing Zhang, 
  • Received Date: 30 December 2024
    Accepted Date: 12 February 2025

  • DNA computing, an innovative technology that utilizes biological molecules to execute computational tasks such as data storage, problem-solving, and logical operations, offers novel pathways to transcend the limitations of conventional computing. As DNA computing continues to evolve, challenges pertaining to complexity, error rates, and computational efficiency have become increasingly pronounced. The advent of artificial intelligence (AI) presents significant opportunities to optimize and enhance DNA computing. Particularly in the realms of data analysis, model optimization, and error correction, the integration of AI technologies has markedly improved the efficiency, accuracy, and stability of DNA computing. This paper provides an in-depth review of AI algorithm classifications and examines how AI empowers DNA computing, with a particular emphasis on optimizing computational workflows, enhancing logic gate functionality, and refining error correction mechanisms. Additionally, we summarize the pivotal applications of AI and DNA computing at the forefront of bioinformatics, including genomics, protein structure prediction, disease diagnosis, and precision medicine. Looking ahead, the ongoing innovation in AI and DNA computing is anticipated to further broaden their application scope, positioning them as a transformative force in the advancement of biosciences.
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