Flyer

International Journal of Drug Development and Research

  • ISSN: 0975-9344
  • Journal h-index: 51
  • Journal CiteScore: 46.50
  • Journal Impact Factor: 26.99
  • Average acceptance to publication time (5-7 days)
  • Average article processing time (30-45 days) Less than 5 volumes 30 days
    8 - 9 volumes 40 days
    10 and more volumes 45 days
Awards Nomination 20+ Million Readerbase
Indexed In
  • Genamics JournalSeek
  • China National Knowledge Infrastructure (CNKI)
  • CiteFactor
  • Scimago
  • Directory of Research Journal Indexing (DRJI)
  • OCLC- WorldCat
  • Publons
  • MIAR
  • University Grants Commission
  • Euro Pub
  • Google Scholar
  • J-Gate
  • SHERPA ROMEO
  • Secret Search Engine Labs
  • ResearchGate
  • International Committee of Medical Journal Editors (ICMJE)
Share This Page

Puneet Souda

Department of Pharmaceutical and Drug Discovery, Faculty of Science, King Mongkut’s University of Technology Thonburi, Thailand

Publications
  • Research Article   
    Extraction of Drug-Drug Interactions Using Convolutional Neural Networks
    Author(s): Puneet Souda*

    Drug-drug interaction (DDI) extraction has long been a popular relation extraction task in natural language processing (NLP). Modern support vector machines (SVM) with a high number of manually set features are the foundation of most DDI extraction methods. Convolutional neural networks (CNN), a reliable machine learning technique that nearly never requires manually generated features, have recently shown significant promise for a variety of NLP tasks. CNN should be used for DDI extraction, which has never been looked at. A CNN-based technique for DDI extraction was put forth. CNN is a good option for DDI extraction, as shown by experiments done on the 2013 DDI Extraction challenge corpus. The CNN-based DDI extraction approach outperforms the currently highest performing method by 69.75%, achieving a score of 69.75%. Keywords Drug-drug i.. View More»

    DOI: 10.36648-0975-9344- 15.2-993

    Abstract HTML PDF