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Natural Language Processing for Drug Discovery Knowledge Graphs: Promises and Pitfalls

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High Performance Computing for Drug Discovery and Biomedicine

Part of the book series: Methods in Molecular Biology ((MIMB,volume 2716))

Abstract

Building and analyzing knowledge graphs (KGs) to aid drug discovery is a topical area of research. A salient feature of KGs is their ability to combine many heterogeneous data sources in a format that facilitates discovering connections. The utility of KGs has been exemplified in areas such as drug repurposing, with insights made through manual exploration and modeling of the data. In this chapter, we discuss promises and pitfalls of using natural language processing (NLP) to mine “unstructured text”— typically from scientific literature— as a data source for KGs. This draws on our experience of initially parsing “structured” data sources—such as ChEMBL—as the basis for data within a KG, and then enriching or expanding upon them using NLP. The fundamental promise of NLP for KGs is the automated extraction of data from millions of documents—a task practically impossible to do via human curation alone. However, there are many potential pitfalls in NLP-KG pipelines, such as incorrect named entity recognition and ontology linking, all of which could ultimately lead to erroneous inferences and conclusions.

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Authors and Affiliations

  1. Evotec (UK) Ltd., in silico Research and Development, Abingdon, Oxfordshire, UK

    J. Charles G. Jeynes, Tim James & Matthew Corney

Authors
  1. J. Charles G. Jeynes
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  2. Tim James
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  3. Matthew Corney
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Corresponding authors

Correspondence to J. Charles G. Jeynes or Tim James .

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Editors and Affiliations

  1. In Silico Research and Development, Evotec UK Ltd, Abingdon, UK

    Alexander Heifetz

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© 2024 The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature

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Jeynes, J.C.G., James, T., Corney, M. (2024). Natural Language Processing for Drug Discovery Knowledge Graphs: Promises and Pitfalls. In: Heifetz, A. (eds) High Performance Computing for Drug Discovery and Biomedicine. Methods in Molecular Biology, vol 2716. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-3449-3_10

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  • DOI: https://doi.org/10.1007/978-1-0716-3449-3_10

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  • Publisher Name: Humana, New York, NY

  • Print ISBN: 978-1-0716-3448-6

  • Online ISBN: 978-1-0716-3449-3

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