Presentation 2003/9/7
Classification of Pharmacological Activity of Drugs Using Support Vector Machines
Yoshimasa TAKAHASHI, Katsumi NISHIKOORI, Satoshi FUJISHIMA,
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Abstract(in English) In the present work, we investigated an applicability of Support Vector Machine (SVM) for classification of pharmacological activities of drugs. The numerical description of chemical structure of each drug was based the Topological Fragment Spectra (TFS) which was reported in our preceding work. Dopamine antagonists of 1,228 that interact with different type of receptors (D1, D2, D3 and D4) were used for training the SVM. For a prediction set of 136 drugs that were not contained in the training set, the SVM model classified 89.8% of the drugs into their own activity classes correctly.
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Keyword(in English) Pattern Classification / SVM / Dopamine Antagonists / Topological Fragment Spectra / Structure-Activity Relationship / Risk Report
Paper # AI2003-42
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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) Classification of Pharmacological Activity of Drugs Using Support Vector Machines
Sub Title (in English)
Keyword(1) Pattern Classification
Keyword(2) SVM
Keyword(3) Dopamine Antagonists
Keyword(4) Topological Fragment Spectra
Keyword(5) Structure-Activity Relationship
Keyword(6) Risk Report
1st Author's Name Yoshimasa TAKAHASHI
1st Author's Affiliation Department of Knowledge-based Inforamtion Engineering, Toyohashi University of Technology()
2nd Author's Name Katsumi NISHIKOORI
2nd Author's Affiliation Department of Knowledge-based Inforamtion Engineering, Toyohashi University of Technology
3rd Author's Name Satoshi FUJISHIMA
3rd Author's Affiliation Department of Knowledge-based Inforamtion Engineering, Toyohashi University of Technology
Date 2003/9/7
Paper # AI2003-42
Volume (vol) vol.103
Number (no) 304
Page pp.pp.-
#Pages 4
Date of Issue