| Project/Area Number |
18590140
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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| Allocation Type | Single-year Grants |
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| Section | 一般 |
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| Research Field |
Medical pharmacy
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| Research Institution | Kyoto University |
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Principal Investigator |
YAMASHITA Fumiyoshi Kyoto University, Graduate School of Pharmaceutical Sciences, Associate Professor (30243041)
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| Project Period (FY) |
2006 – 2007
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| Project Status |
Completed (Fiscal Year 2007)
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| Budget Amount *help |
¥3,870,000 (Direct Cost: ¥3,600,000、Indirect Cost: ¥270,000)
Fiscal Year 2007: ¥1,170,000 (Direct Cost: ¥900,000、Indirect Cost: ¥270,000)
Fiscal Year 2006: ¥2,700,000 (Direct Cost: ¥2,700,000)
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| Keywords | drug metabolism / text mining / natural language processing / structure-activity relationship / information visualization / discrimination analysis / 決定木モデル / 階層型データ / 多目的同時最適化 / 遺伝的アルゴリズム / 化合物情報 |
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| Research Abstract |
Drug metabolism is an important factor determining disposition behavior of the drug in its action sites. Moreover, genetic epigenetic difference of activity of drug metabolizing enzymes causes inter-individual variability in drug responses. Therefore, it is an important issue to predict how drugs or new chemical entities are metabolized. Although experts in this field have been manually collecting and analyzing information of drug metabolism, there have been few databases covering dozens of drug metabolizing enzymes and their interaction with chemicals. The present study was initiated to obtain the knowledge of structure-activity relationship of drug metabolism through comprehensive information acquisition from the literatures. I developed a natural language processing system for collecting information on chemical-enzyme interaction, which comprises dictionaries of chemicals and rule bases for retrieval of unregistered chemicals. The system can also detect how the chemicals interact with enzymes, simply by defining the names of the enzymes. In addition, I developed an algorithm of simultaneous analysis and visualization of structure-activity relationship towards multiple drug metabolizing enzymes. These methods would help us to proceed drug discovery and development effectively.
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