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Graph Neural Networks for Molecules

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Machine Learning in Molecular Sciences

Part of the book series: Challenges and Advances in Computational Chemistry and Physics ((COCH,volume 36))

Abstract

Graph neural networks (GNNs), which are capable of learning representations from graphical data, are naturally suitable for modeling molecular systems. This review introduces GNNs and their various applications for small organic molecules. GNNs rely on message-passing operations, a generic yet powerful framework, to update node features iteratively. Many researches design GNN architectures to effectively learn topological information of 2D molecule graphs as well as geometric information of 3D molecular systems. GNNs have been implemented in a wide variety of molecular applications, including molecular property prediction, molecular scoring and docking, molecular optimization and de novo generation, molecular dynamics simulation, etc. Besides, the review also summarizes the recent development of self-supervised learning for molecules with GNNs.

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Notes

  1. 1.

    https://www.rdkit.org/.

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Authors and Affiliations

  1. Department of Mechanical Engineering, Carnegie Mellon University, Pittsburgh, PA, 15213, USA

    Yuyang Wang, Zijie Li & Amir Barati Farimani

  2. Machine Learning Department, Carnegie Mellon University, Pittsburgh, PA, 15213, USA

    Yuyang Wang & Amir Barati Farimani

  3. Department of Chemical Engineering, Carnegie Mellon University, Pittsburgh, PA, 15213, USA

    Amir Barati Farimani

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  1. Yuyang Wang
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Correspondence to Amir Barati Farimani .

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    Chen Qu

  2. Google Inc, Mountain View, CA, USA

    Hanchao Liu

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Wang, Y., Li, Z., Barati Farimani, A. (2023). Graph Neural Networks for Molecules. In: Qu, C., Liu, H. (eds) Machine Learning in Molecular Sciences. Challenges and Advances in Computational Chemistry and Physics, vol 36. Springer, Cham. https://doi.org/10.1007/978-3-031-37196-7_2

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