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2016 Fiscal Year Final Research Report

Computational drug discovery techniques with systematic target identification and high-performance molecular simulation

Research Project

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Project/Area Number 24240044
Research Category

Grant-in-Aid for Scientific Research (A)

Allocation TypeSingle-year Grants
Section一般
Research Field Bioinformatics/Life informatics
Research InstitutionTokyo Institute of Technology

Principal Investigator

Akiyama Yutaka  東京工業大学, 情報理工学院, 教授 (30243091)

Co-Investigator(Kenkyū-buntansha) 瀬々 潤  国立研究開発法人産業技術総合研究所, その他部局等, 研究員 (40361539)
関嶋 政和  東京工業大学, 学術国際情報センター, 准教授 (80371053)
Project Period (FY) 2012-04-01 – 2017-03-31
Keywords生体生命情報学 / アルゴリズム / 薬学 / ハイパフォーマンス・コンピューティング / 分子シミュレーション
Outline of Final Research Achievements

Several methodologies are developed to support computational drug discovery. In Theme-1, Sese et.al have developed the LAMP algorithm for correctly estimating statistical significance in combinatorial regulations, and apply the technique for identifying key genes in human breast cancer cells. In Theme-2, Akiyama et.al have developed MEGADOCK docking software for massively PPI docking study and built an archiving database for human protein interactions with tertiary docking structures. They also developed an ultra-fast compound pre-screening software called Spresso based on fragment-based docking, In Theme-3, Sekijima et.al have studied molecular dynamics techniques to obtain adequate protein structures for virtual screening. They evaluated the techniques in real drug discovery projects and revealed that open-form structures obtained by molecular dynamics and clustering perform much better than using the closed X-ray structures, in their virtual screening followed by wet experiments.

Free Research Field

バイオインフォマティクス

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Published: 2018-03-22  

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