Abstract

Drug-target interactions (DTIs) are critical for drug repurposing and elucidation of drug mechanisms, and are manually curated by large databases, such as ChEMBL, BindingDB, DrugBank and DrugTargetCommons. However, the number of curated articles likely constitutes only a fraction of all the articles that contain experimentally determined DTIs. Finding such articles and extracting the experimental information is a challenging task, and there is a pressing need for systematic approaches to assist the curation of DTIs. To this end, we applied Bidirectional Encoder Representations from Transformers (BERT) to identify such articles. Because DTI data intimately depends on the type of assays used to generate it, we also aimed to incorporate functions to predict the assay format.
Original languageEnglish
Article number245
JournalBMC Bioinformatics
Volume23
Issue number1
Number of pages13
ISSN1471-2105
DOIs
Publication statusPublished - 21 Jun 2022
MoE publication typeA1 Journal article-refereed

Fields of Science

  • BERT
  • BERT for biomedical data
  • Bidirectional encoder representations from transformers
  • Bioactivity data
  • Biomedical text mining
  • Drug repurposing
  • Drug target interaction prediction
  • INFORMATION
  • Mining drug target interactions
  • PREDICTION
  • 3111 Biomedicine

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