• DocumentCode
    1943005
  • Title

    Neural System for in silico Drug-Drug Interaction Screening

  • Author

    Polak, Sebastian ; Brandys, Jerzy ; Mendyk, Aleksander

  • Author_Institution
    Dept. of Pharmacoepidemiology & Pharmacoeconomics, Jagiellonian Univ., Cracow
  • Volume
    2
  • fYear
    2005
  • fDate
    28-30 Nov. 2005
  • Firstpage
    75
  • Lastpage
    80
  • Abstract
    Drug usage is always associated with risk drugs interactions are considered to be one of the potential sources of undesirable action of drugs. Such a situation enforced U.S. Food and Drug Administration (FDA) as well as European Medicines Agency (EMEA) to issue a guidance for industry and researchers for in vivo and in vitro drug interactions studies. The authors proposed neural networks based in silico system for potential drug-drug interactions screening with use of simple physico-chemical data describing each chemical substance particle. Initial results where 77% of classification rate in generalization test was found suggest that computational intelligence based systems could be effective in this area
  • Keywords
    drugs; learning (artificial intelligence); neural chips; pharmaceutical industry; EMEA; European Medicines Agency; FDA; U.S. Food and Drug Administration; computational intelligence based system; drug usage; neural network; physico-chemical data; silico drug-drug interaction screening; Artificial neural networks; Biological system modeling; Computational intelligence; Drugs; Educational institutions; Food industry; In vitro; In vivo; Neurons; Pharmaceutical technology;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Computational Intelligence for Modelling, Control and Automation, 2005 and International Conference on Intelligent Agents, Web Technologies and Internet Commerce, International Conference on
  • Conference_Location
    Vienna
  • Print_ISBN
    0-7695-2504-0
  • Type

    conf

  • DOI
    10.1109/CIMCA.2005.1631448
  • Filename
    1631448