DrugMetab: An Integrated Machine Learning and Lexicon Mapping Named Entity Recognition Method for Drug Metabolite

Heng Yi Wu, Deshun Lu, Mustafa Hyder, Shijun Zhang, Sara K. Quinney, Zeruesenay Desta, Lang Li

Research output: Contribution to journalArticle

Abstract

Drug metabolites (DMs) are critical in pharmacology research areas, such as drug metabolism pathways and drug-drug interactions. However, there is no terminology dictionary containing comprehensive drug metabolite names, and there is no named entity recognition (NER) algorithm focusing on drug metabolite identification. In this article, we developed a novel NER system, DrugMetab, to identify DMs from the PubMed abstracts. DrugMetab utilizes the features characterized from the Part-of-Speech, drug index, and pre/suffix, and determines DMs within context. To evaluate the performance, a gold-standard corpus was manually constructed. In this task, DrugMetab with sequential minimal optimization (SMO) classifier achieves 0.89 precision, 0.77 recall, and 0.83 F-measure in the internal testing set; and 0.86 precision, 0.85 recall, and 0.86 F-measure in the external validation set. We further compared the performance between DrugMetab and whatizitChemical, which was designed for identifying small molecules or chemical entities. DrugMetab outperformed whatizitChemical, which had a lower recall rate of 0.65.

Original languageEnglish (US)
Pages (from-to)709-717
Number of pages9
JournalCPT: Pharmacometrics and Systems Pharmacology
Volume7
Issue number11
DOIs
StatePublished - Nov 2018

ASJC Scopus subject areas

  • Modeling and Simulation
  • Pharmacology (medical)

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