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Graph Neural Networks in Cheminformatics

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Intelligent Computing and Optimization (ICO 2020)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1324))

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Abstract

Graph neural networks represent nowadays the most effective machine learning technology in the biochemistry domain. Learning on the huge amount of chemical data can take an important part in finding new molecules or new drugs, which is a crucial research work in cheminformatics. This work would be no more time-consuming and labor-intensive with the assistant of machine learning techniques: they are capable of both handling massive datasets and learning the hidden information from the structure of graphs. In terms of applying machine learning of graphs in chemistry, this paper discusses the explorations on the following matters. Firstly, we introduce the up-to-date study of the machine learning approaches being applied in solving cheminformatics research problems. Secondly, we present concise overviews on the original mathematical model and variants of graph neural networks and the utilization in drug discovery evaluating the performance with machine learning. We end our analysis with a critical discussion of potential research based on current literature reviews and suggestions for relevant approaches and challenges.

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Acknowledgement

This work is supported by the Fundamental Research Grant Scheme (FRGS) of the Ministry of Higher Education Malaysia under the grant project number FRGS/1/2019/ICT02/KDUPG/02/1.

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Authors and Affiliations

  1. UOW Malaysia KDU Penang University College, Penang, Malaysia

    H. N. Tran Tran, J. Joshua Thomas, Abdalla M. Ali & Son Bach Huynh

  2. Universiti Sains Malaysia, Penang, Malaysia

    Nurul Hashimah Ahamed Hassain Malim

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  1. H. N. Tran Tran
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  2. J. Joshua Thomas
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  4. Abdalla M. Ali
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Correspondence to J. Joshua Thomas .

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Editors and Affiliations

  1. Department of Fundamental and Applied Sciences, Universiti Teknologi Petronas, Tronoh, Perak, Malaysia

    Pandian Vasant

  2. Faculty of Electrical Engineering and Computer Science, VÅ B TU Ostrava, Ostrava, Czech Republic

    Ivan Zelinka

  3. Faculty of Engineering Management, Poznan University of Technology, Poznan, Poland

    Gerhard-Wilhelm Weber

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Tran, H.N.T., Joshua Thomas, J., Malim, N.H.A.H., Ali, A.M., Huynh, S.B. (2021). Graph Neural Networks in Cheminformatics. In: Vasant, P., Zelinka, I., Weber, GW. (eds) Intelligent Computing and Optimization. ICO 2020. Advances in Intelligent Systems and Computing, vol 1324. Springer, Cham. https://doi.org/10.1007/978-3-030-68154-8_71

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