Presentation 2003/9/7
Knowledge Discovery using Attributes from 3D Molecular Structures : Importance of Active User's Response
Takashi OKADA, Masumi YAMAKAWA, Hirotaka NIITSUMA, Naomi KAMIGUCHI,
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Abstract(in English) Active responses from experts plays an essential role in the knowledge discovery of SAR (structure activity relationships) from drug data. Experts often think of hypotheses, and they want to reflect these ideas to the attribute generation and selection process. Authors have analyzed SAR of dopamine antagonists using the cascade model. In this paper, we generated attributes indicating the presence of hydrogen-bonded fragments from 3D coordinates of molecules, which were suggested by experts. The selection of attributes by experts has been shown to be useful in obtaining valuable knowledge.
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Keyword(in English) Attribute Generation and Selection / Cascade Model / Dopamine / 3D Molecular Structure / Hydrogen-bond
Paper # AI2003-37
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Committee AI
Conference Date 2003/9/7(1days)
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Registration To Artificial Intelligence and Knowledge-Based Processing (AI)
Language ENG
Title (in Japanese) (See Japanese page)
Sub Title (in Japanese) (See Japanese page)
Title (in English) Knowledge Discovery using Attributes from 3D Molecular Structures : Importance of Active User's Response
Sub Title (in English)
Keyword(1) Attribute Generation and Selection
Keyword(2) Cascade Model
Keyword(3) Dopamine
Keyword(4) 3D Molecular Structure
Keyword(5) Hydrogen-bond
1st Author's Name Takashi OKADA
1st Author's Affiliation Department of Informatics, Kwansei Gakuin University()
2nd Author's Name Masumi YAMAKAWA
2nd Author's Affiliation Department of Informatics, Kwansei Gakuin University
3rd Author's Name Hirotaka NIITSUMA
3rd Author's Affiliation Department of Informatics, Kwansei Gakuin University
4th Author's Name Naomi KAMIGUCHI
4th Author's Affiliation Osaka Research Ctr., Takeda Chemical Industries
Date 2003/9/7
Paper # AI2003-37
Volume (vol) vol.103
Number (no) 304
Page pp.pp.-
#Pages 5
Date of Issue