| Project/Area Number |
25870336
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| Research Category |
Grant-in-Aid for Young Scientists (B)
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| Allocation Type | Multi-year Fund |
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| Research Field |
Life / Health / Medical informatics
System genome science
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| Research Institution | Kyoto University |
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Principal Investigator |
Brown John 京都大学, 医学研究科, 講師 (90583188)
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| Research Collaborator |
SCHNEIDER Gisbert ETH Zurich
REKER Daniel Massachusetts Institute of Technology
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| Project Period (FY) |
2013-04-01 – 2017-03-31
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| Project Status |
Completed (Fiscal Year 2016)
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| Budget Amount *help |
¥4,160,000 (Direct Cost: ¥3,200,000、Indirect Cost: ¥960,000)
Fiscal Year 2016: ¥1,040,000 (Direct Cost: ¥800,000、Indirect Cost: ¥240,000)
Fiscal Year 2015: ¥910,000 (Direct Cost: ¥700,000、Indirect Cost: ¥210,000)
Fiscal Year 2014: ¥910,000 (Direct Cost: ¥700,000、Indirect Cost: ¥210,000)
Fiscal Year 2013: ¥1,300,000 (Direct Cost: ¥1,000,000、Indirect Cost: ¥300,000)
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| Keywords | chemogenomics / comput. drug discovery / pattern recognition / life science informatics / ケモジェノミクス / 計算創薬 / 統計パターン認識 / 生命情報科学 / 化合物 / タンパク質 / 相互作用 / 遺伝子発言 / 個別化化学療法 / マイクロアレイ / トランスクリプトーム / 分子相互作用 / 臨床情報 / オミックス / ケモジェノミックス / バーチャルスクリーニング / 遺伝子発現 / 機械学習 / 分子ネットワーク / 国際研究者交流 ドイツ / 国際研究者交流 フランス |
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| Outline of Final Research Achievements |
In this project, we executed research for computational drug discovery. The fundamental principle of most drugs is that a compound works to inhibit the function of a particular protein. To know in advance which compounds will inhibit which proteins is difficult, but in recent years computational methods have become developed which are expected to bring down drug development costs.
In this research we developed a new method for the prediction of compound-protein interactions. Among its many features, ours is special in that it requires neither big data nor complex artificial intelligence (AI), yet can still build a highly predictive model. We disproved the current trend of "big data drug discovery" by developing a reproducible method and reported multiple scientific papers. We are planning with pharmaceutical companies to apply the technology to joint research projects.
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