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ファイル | 記述 | サイズ | フォーマット | |
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S0219720010005117.pdf | 778.43 kB | Adobe PDF | 見る/開く |
タイトル: | COMPOUND ANALYSIS VIA GRAPH KERNELS INCORPORATING CHIRALITY |
著者: | BROWN, J.B. ![]() URATA, TAKASHI TAMURA, TAKEYUKI ![]() ![]() ![]() ARAI, MIDORI A. KAWABATA, TAKEO ![]() ![]() AKUTSU, TATSUYA ![]() ![]() ![]() |
キーワード: | Kernel method graph kernel QSAR QSPR upport vector machine |
発行日: | Dec-2010 |
出版者: | World Scientific Publishing Co. |
誌名: | Journal of Bioinformatics and Computational Biology |
巻: | 08 |
号: | supp01 |
開始ページ: | 63 |
終了ページ: | 81 |
抄録: | High accuracy is paramount when predicting biochemical characteristics using Quantitative Structural-Property Relationships (QSPRs). Although existing graph-theoretic kernel methods combined with machine learning techniques are efficient for QSPR model construction, they cannot distinguish topologically identical chiral compounds which often exhibit different biological characteristics. In this paper, we propose a new method that extends the recently developed tree pattern graph kernel to accommodate stereoisomers. We show that Support Vector Regression (SVR) with a chiral graph kernel is useful for target property prediction by demonstrating its application to a set of human vitamin D receptor ligands currently under consideration for their potential anti-cancer effects. |
著作権等: | © 2011 World Scientific Publishing Co. この論文は出版社版でありません。引用の際には出版社版をご確認ご利用ください。 This is not the published version. Please cite only the published version. |
URI: | http://hdl.handle.net/2433/134572 |
DOI(出版社版): | 10.1142/S0219720010005117 |
PubMed ID: | 21155020 |
出現コレクション: | 学術雑誌掲載論文等 |
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