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Machine Learning in Molecular Dynamics Simulation

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Proceedings of 2023 Chinese Intelligent Systems Conference (CISC 2023)

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 1090))

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Abstract

Molecular dynamics simulation is a powerful tool to study biological problems, such as protein-ligand binding, protein folding/unfolding, flexibility of biomolecules, free energy calculations, etc. It also plays an important role in drug design in identifying potential small molecules that binds to target. As the surging development of machine learning in recent years, many possibilities in molecular dynamics simulation become visible.

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Acknowledgements

X.T. acknowledges support from the National Institutes of Health through Grant No. R01-GM122441, and the mentoring of Toshiko Ichiye.

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Authors and Affiliations

  1. Department of Chemistry, Georgetown University, Washington, DC, 20057, USA

    Xiaojing Teng

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  1. Xiaojing Teng
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Correspondence to Xiaojing Teng .

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Editors and Affiliations

  1. School of Automation Science and Electrical Engineering, Beihang University, Beijing, China

    Yingmin Jia

  2. School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing, China

    Weicun Zhang

  3. School of Mechanical Engineering and Automation, Beihang University, Beijing, China

    Yongling Fu

  4. Ningbo Institute of Materials Technology&Engineering, Chinese Academy of Sciences, Ningbo, Zhejiang, China

    Jiqiang Wang

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Teng, X. (2023). Machine Learning in Molecular Dynamics Simulation. In: Jia, Y., Zhang, W., Fu, Y., Wang, J. (eds) Proceedings of 2023 Chinese Intelligent Systems Conference. CISC 2023. Lecture Notes in Electrical Engineering, vol 1090. Springer, Singapore. https://doi.org/10.1007/978-981-99-6882-4_52

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