Learning on Structure-Activity Relationship from Heterogenous Chemical Compound Databases
| Project/Area Number |
17K00320
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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| Allocation Type | Multi-year Fund |
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| Section | 一般 |
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| Research Field |
Intelligent informatics
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| Research Institution | Kwansei Gakuin University |
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Principal Investigator |
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| Co-Investigator(Kenkyū-buntansha) |
田中 大輔 関西学院大学, 理工学部, 准教授 (60589399)
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| Project Period (FY) |
2017-04-01 – 2021-03-31
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| Project Status |
Completed (Fiscal Year 2020)
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| Budget Amount *help |
¥4,550,000 (Direct Cost: ¥3,500,000、Indirect Cost: ¥1,050,000)
Fiscal Year 2019: ¥1,430,000 (Direct Cost: ¥1,100,000、Indirect Cost: ¥330,000)
Fiscal Year 2018: ¥1,300,000 (Direct Cost: ¥1,000,000、Indirect Cost: ¥300,000)
Fiscal Year 2017: ¥1,820,000 (Direct Cost: ¥1,400,000、Indirect Cost: ¥420,000)
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| Keywords | 機械学習 / データベース / グラフ / 構造活性相関 / グラフ分類 / クラスタリング / データマイニング / 深層学習 / ケモインフォマティクス |
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| Outline of Final Research Achievements |
In this work, we investigated various approaches for learning on structure-activity relationship from heterogenous chemical compound databases. The first outcome is establishing a methodology for efficiently searching graphs contained in a query graph from a database consisting of a huge amount of graphs. Our approach is based on the prefix tree of graph codes that represent the graphs in the database. By using the prefix tree as an index, we simultaneously compute the subgraph isomorphism problem (which is known to be NP-complete) between the query graph and multiple graphs in the database. The second outcome is reducing the over smoothing phenomenon in Graph Convolution Networks in the deep learning research domain. In our approach, we combined Graph Convolution networks with the dense connection, and increased a certain percentage of prediction accuracies for various benchmark datasets compared with some conventional methods.
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| Academic Significance and Societal Importance of the Research Achievements |
深層学習法は，現在，盛んに研究が実施されている研究分野である．その中で，様々なタイプのデータが解析されているが，グラフは非常に高い表現力を有していることが知られ，それに対するデータ解析手法や学習手法は非常に重要である．我々の成果は，データベースと機械学習の基礎研究分野への貢献であり，それらを発展させていくことで，実用化を目指せるものである．実用化の具体例の1つは，創薬化学分野である．
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Report
(5 results)
Research Products
(14 results)
