Predicting DNA Reactions with a Quantum Chemistry-Based Deep Learning Model.

Journal: Advanced science (Weinheim, Baden-Wurttemberg, Germany)
PMID: 39297371

Abstract

In this study, a deep learning model based on quantum chemistry is introduced to enhance the accuracy and efficiency of predicting DNA reaction parameters. By integrating quantum chemical calculations with self-designed descriptor matrices, the model offers a comprehensive description of energy variations and considers a broad range of relevant factors. To overcome the challenge of limited labeled data, an active learning method is employed. The results demonstrate that this model outperforms existing methods in predicting DNA hybridization free energies and strand displacement rate constants, thus advancing the understanding of DNA molecular interactions, and aiding in the precise design and optimization of DNA-based systems.

Authors

  • Likun Wang
    Shanghai Key Laboratory of Green Chemistry and Chemical Processes, Shanghai Engineering Research Center of Molecular Therapeutics and New Drug Development, School of Chemistry and Molecular Engineering, East China Normal University, Shanghai, 200241, China.
  • Na Li
    School of Nursing, Fujian University of Traditional Chinese Medicine, Fuzhou, China.
  • Mengyao Cao
    Shanghai Key Laboratory of Green Chemistry and Chemical Processes, Shanghai Engineering Research Center of Molecular Therapeutics and New Drug Development, School of Chemistry and Molecular Engineering, East China Normal University, Shanghai, 200241, China.
  • Yun Zhu
    College of Environment and Energy, South China University of Technology, Guangzhou Higher Education Mega Center, Guangzhou 510006, China.
  • Xiewei Xiong
    Shanghai Key Laboratory of Green Chemistry and Chemical Processes, Shanghai Engineering Research Center of Molecular Therapeutics and New Drug Development, School of Chemistry and Molecular Engineering, East China Normal University, Shanghai, 200241, China.
  • Li Li
    Department of Gastric Surgery, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, Affiliated Cancer Hospital of University of Electronic Science and Technology of China, Chengdu, China.
  • Tong Zhu
  • Hao Pei
    Shanghai Key Laboratory of Green Chemistry and Chemical Processes, School of Chemistry and Molecular Engineering, Shanghai Frontiers Science Center of Genome Editing and Cell Therapy, East China Normal University, 500 Dongchuan Road, Shanghai 200241, P. R. China.

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