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

2016 Fiscal Year Final Research Report

• Project/Area Number: 24240044
• Research Category: Grant-in-Aid for Scientific Research (A)
• Allocation Type: Single-year Grants
• Section: 一般
• Research Field: Bioinformatics/Life informatics
• Research Institution: Tokyo 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

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