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http://dx.doi.org/10.15207/JKCS.2019.10.3.023

A machine learning model for the derivation of major molecular descriptor using candidate drug information of diabetes treatment  

Namgoong, Youn (Department of Convergence Technology & Management Engineering, Yonsei University)
Kim, Chang Ouk (Department of Industrial Engineering, Yonsei University)
Lee, Chang Joon (Dotmatics, Co., Ltd)
Publication Information
Journal of the Korea Convergence Society / v.10, no.3, 2019 , pp. 23-30 More about this Journal
Abstract
The purpose of this study is to find out the structure of the substance that affects antidiabetic using the candidate drug information for diabetes treatment. A quantitative structure activity relationship model based on machine learning method was constructed and major molecular descriptors were determined for each experimental data variables from coefficient values using a partial least squares algorithm. The results of the analysis of the molecular access system fingerprint data reflecting the candidate drug structure information were higher than those of the in vitro data analysis in terms of goodness-of-fit, and the major molecular expression factors affecting the antidiabetic effect were also variously derived. If the proposed method is applied to the new drug development environment, it is possible to reduce the cost for conducting candidate screening experiment and to shorten the search time for new drug development.
Keywords
Candidate drug information; Partial least square; Variable importance in projection; Finding major molecular descriptor; Antidiabetic prediction;
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Times Cited By KSCI : 6  (Citation Analysis)
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