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