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Boosting Similar Compounds Searches via Correlated Subgraph Analysis

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Information Integration and Web Intelligence (iiWAS 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14416))

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Abstract

Graph similarity search (GSS) models chemical compounds as a graph database. GSS is an essential tool for drug discovery because they can find similar graphs (compounds) for a query. Existing GSS methods have two critical limitations. First, handling large databases is time consuming. Second, finding compounds with the structure-activity relationship (SAR), which is vital in drug discovery, remains difficult. Herein a novel graph-based method for chemical compound searches is proposed to overcome these limitations. Since compounds with SAR share similar substructures, the proposed method extracts correlated subgraphs included in a query and explores similar compounds. In practical drug discovery task, our method achieves faster searches and improved accuracy compared to existing methods.

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Acknowledgements

This work was partly supported by JST PRESTO (JPMJPR2033) and JSPS KAKENHI (JP22K17894).

Author information

Authors and Affiliations

  1. Department of Computer Science, University of Tsukuba, Tsukuba, Japan

    Yuma Naoi

  2. Center for Computational Sciences, University of Tsukuba, Tsukuba, Japan

    Hiroaki Shiokawa

Authors
  1. Yuma Naoi
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  2. Hiroaki Shiokawa
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Corresponding author

Correspondence to Yuma Naoi .

Editor information

Editors and Affiliations

  1. Monash University, Melbourne, VIC, Australia

    Pari Delir Haghighi

  2. La Trobe University, Melbourne, VIC, Australia

    Eric Pardede

  3. University of Auckland, Auckland, New Zealand

    Gillian Dobbie

  4. University of Auckland, Auckland, New Zealand

    Vithya Yogarajan

  5. Udayana University, Denpasar, Indonesia

    Ngurah Agus Sanjaya ER

  6. Johannes Kepler University Linz, Linz, Austria

    Gabriele Kotsis

  7. Johannes Kepler University Linz, Linz, Austria

    Ismail Khalil

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Naoi, Y., Shiokawa, H. (2023). Boosting Similar Compounds Searches via Correlated Subgraph Analysis. In: Delir Haghighi, P., et al. Information Integration and Web Intelligence. iiWAS 2023. Lecture Notes in Computer Science, vol 14416. Springer, Cham. https://doi.org/10.1007/978-3-031-48316-5_42

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  • DOI: https://doi.org/10.1007/978-3-031-48316-5_42

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-48315-8

  • Online ISBN: 978-3-031-48316-5

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