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
Link To Document :
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