Automated Information Extraction and Structure鈭扐ctivity Relationship Analysis of Cytochrome P450 Substrates

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• 作者：Fumiyoshi Yamashita ; Chunlai Feng ; Shuya Yoshida ; Takayuki Itoh ; Mitsuru Hashida
• 刊名：Journal of Chemical Information and Modeling
• 出版年：2011
• 出版时间：February 28, 2011
• 年：2011
• 卷：51
• 期：2
• 页码：378-385
• 全文大小：909K
• 年卷期：v.51,no.2(February 28, 2011)
• ISSN：1549-960X

文摘

Information on CYP鈭抍hemical interactions was comprehensively explored by a text-mining technique, to confirm our previous structure鈭抋ctivity relationship model for CYP substrates (Yamashita et al. J. Chem. Inf. Model. 2008, 48, 364鈭?69). The text-mining technique is based on natural language processing and can extract chemical names and their interaction patterns according to sentence context. After chemicals were automatically extracted and classified into CYP substrates, inhibitors, and inducers, 709 substrates were retrieved from the PubChem database and categorized as 216, 145, 136, 217, 156, and 379 substrates for CYP1A2, CYP2C9, CYP2C19, CYP2D6, CYP2E1, and CYP3A4, respectively. Although the previous classification model was developed using data from only 161 compounds, the model classified the substrates found by text-mining analysis with reasonable accuracy. This confirmed the validity of both the multi-objective classification model for CYP substrates and the text-mining procedure.