Title
Automated Information Extraction and Structure-Activity Relationship Analysis of Cytochrome P450 Substrates.
Abstract
Information on CYP-chemical interactions was comprehensively explored by a text-mining technique, to confirm our previous structure activity relationship model for CYP substrates (Yamashita et al. J. Chem, Inf. Model, 2008, 48, 364-369). 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.
Year
DOI
Venue
2011
10.1021/ci100334z
JOURNAL OF CHEMICAL INFORMATION AND MODELING
DocType
Volume
Issue
Journal
51
2
ISSN
Citations 
PageRank 
1549-9596
3
0.46
References 
Authors
0
5
Name
Order
Citations
PageRank
Fumiyoshi Yamashita1153.67
Chunlai Feng251.17
Shuya Yoshida331.14
Takayuki Itoh450365.85
Mitsuru Hashida5102.71