• DocumentCode
    234626
  • Title

    Drug design: The machine learning roles

  • Author

    El-Telbany, Mohammed E. ; Rafat, Samah ; Nasr, Engy Ebrahim

  • Author_Institution
    Comput. & Syst. Dept., Electron. Res. Inst., Giza, Egypt
  • fYear
    2014
  • fDate
    19-20 April 2014
  • Firstpage
    1
  • Lastpage
    6
  • Abstract
    QSAR (quantitative structure-activity relationship) modeling is one of the well developed areas in drug development through computational chemistry. Similar molecules with just a slight variation in their structure can have quit different biological activity. This kind of relationship between molecular structure and change in biological activity is center of focus for QSAR Modeling. Predictions of property and/or activity of interest have the potential to save time, money and minimize the use of expensive experimental designs, such as, for example, animal testing. This paper, presents a survey of the machine learning algorithms´ roles in the field of QSAR modeling and their impact on modern drug design processes.
  • Keywords
    drugs; learning (artificial intelligence); product design; product development; production engineering computing; QSAR modeling; biological activity; computational chemistry; drug design; drug development; machine learning; molecular structure; quantitative structure-activity relationship; Biological system modeling; Computational modeling; Drugs; QSAR; drug desig; machine learning; prediction;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Engineering and Technology (ICET), 2014 International Conference on
  • Conference_Location
    Cairo
  • Type

    conf

  • DOI
    10.1109/ICEngTechnol.2014.7016794
  • Filename
    7016794