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Related Concept Videos

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Drug discovery is a multifaceted process involving extensive screening, testing, and optimization of lead compounds to identify potential new drugs for therapeutic use. It combines several approaches, including screening large numbers of natural products, chemical modification of known active molecules, identification of new drug targets, and rational design based on biological mechanisms and drug-receptor structure. These approaches are carried out in both academic research laboratories and...
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During the development of a new pharmaceutical, the manufacturer initially assigns a code name to the drug. Once approved, the drug receives a United States Adopted Name (USAN)—a generic, nonproprietary designation. Upon being listed in the United States Pharmacopeia, this nonproprietary name becomes the drug's official name. Additionally, the manufacturer assigns a proprietary name or trademark, which serves as the brand name under which the drug is marketed. It is worth noting that...
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Related Experiment Video

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

J Charles G Jeynes1, Tim James2, Matthew Corney3

  • 1Evotec (UK) Ltd., in silico Research and Development, Abingdon, Oxfordshire, UK. charlie.jeynes@evotec.com.

Methods in Molecular Biology (Clifton, N.J.)
|September 13, 2023
PubMed
Summary

Knowledge graphs (KGs) enhance drug discovery by integrating diverse data. Natural language processing (NLP) can automate data extraction for KGs from scientific literature, but carries risks of errors.

Keywords:
DatabaseDrugBankHeterogeneous dataNLPNamed entity linkingNamed entity recognitionNormalizationOntologiesPubTatorSemMedDBUnstructured text

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Area of Science:

  • Computational chemistry and cheminformatics
  • Bioinformatics and computational biology
  • Natural Language Processing (NLP) in scientific research

Background:

  • Knowledge graphs (KGs) are increasingly used to integrate heterogeneous data for drug discovery.
  • KGs facilitate the discovery of connections, aiding applications like drug repurposing.
  • Manual data curation for KGs is time-consuming and limits scalability.

Purpose of the Study:

  • To discuss the potential and challenges of using Natural Language Processing (NLP) for KG construction in drug discovery.
  • To explore enriching existing structured data sources (e.g., ChEMBL) with information extracted via NLP from unstructured text.
  • To highlight the promise of automated data extraction from scientific literature for KG expansion.

Main Methods:

  • Utilizing NLP techniques to mine unstructured scientific literature for data relevant to drug discovery.
  • Integrating NLP-extracted data with existing structured databases (e.g., ChEMBL) to build and expand knowledge graphs.
  • Analyzing the process of parsing structured and unstructured data for KG creation.

Main Results:

  • NLP offers a scalable solution for extracting vast amounts of data from scientific literature, surpassing manual curation capabilities.
  • The integration of NLP-derived data can enrich and expand existing knowledge graphs.
  • Potential pitfalls in NLP-KG pipelines, including errors in named entity recognition and ontology linking, were identified.

Conclusions:

  • NLP holds significant promise for automating data extraction and enhancing knowledge graphs in drug discovery.
  • Careful consideration of NLP pipeline limitations is crucial to avoid erroneous inferences.
  • Future work should focus on mitigating NLP-related errors to ensure the reliability of KG-driven drug discovery.