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Machine learning for chemical discovery

Author

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  • Alexandre Tkatchenko

    (University of Luxembourg)

Abstract

Discovering chemicals with desired attributes is a long and painstaking process. Curated datasets containing reliable quantum-mechanical properties for millions of molecules are becoming increasingly available. The development of novel machine learning tools to obtain chemical knowledge from these datasets has the potential to revolutionize the process of chemical discovery. Here, I comment on recent breakthroughs in this emerging field and discuss the challenges for the years to come.

Suggested Citation

  • Alexandre Tkatchenko, 2020. "Machine learning for chemical discovery," Nature Communications, Nature, vol. 11(1), pages 1-4, December.
    • Handle: RePEc:nat:natcom:v:11:y:2020:i:1:d:10.1038_s41467-020-17844-8
    DOI: 10.1038/s41467-020-17844-8
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    Cited by:

    1. Han Li & Ruotian Zhang & Yaosen Min & Dacheng Ma & Dan Zhao & Jianyang Zeng, 2023. "A knowledge-guided pre-training framework for improving molecular representation learning," Nature Communications, Nature, vol. 14(1), pages 1-13, December.
    2. Huziel E. Sauceda & Luis E. Gálvez-González & Stefan Chmiela & Lauro Oliver Paz-Borbón & Klaus-Robert Müller & Alexandre Tkatchenko, 2022. "BIGDML—Towards accurate quantum machine learning force fields for materials," Nature Communications, Nature, vol. 13(1), pages 1-16, December.
    3. Gabriele Orlando & Daniele Raimondi & Ramon Duran-Romaña & Yves Moreau & Joost Schymkowitz & Frederic Rousseau, 2022. "PyUUL provides an interface between biological structures and deep learning algorithms," Nature Communications, Nature, vol. 13(1), pages 1-9, December.
    4. Hao Xu & Jinglong Lin & Dongxiao Zhang & Fanyang Mo, 2023. "Retention time prediction for chromatographic enantioseparation by quantile geometry-enhanced graph neural network," Nature Communications, Nature, vol. 14(1), pages 1-15, December.

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