論文

査読有り
2010年

Graph mining in chemoinformatics

Chemoinformatics and Advanced Machine Learning Perspectives: Complex Computational Methods and Collaborative Techniques
  • Hiroto Saigo
  • ,
  • Koji Tsuda

開始ページ
95
終了ページ
128
記述言語
英語
掲載種別
論文集(書籍)内論文
DOI
10.4018/978-1-61520-911-8.ch006
出版者・発行元
IGI Global

In standard QSAR (Quantitative Structure Activity Relationship) approaches, chemical compounds are represented as a set of physicochemical property descriptors, which are then used as numerical features for classification or regression. However, standard descriptors such as structural keys and fingerprints are not comprehensive enough in many cases. Since chemical compounds are naturally represented as attributed graphs, graph mining techniques allow us to create subgraph patterns (i.e., structural motifs) that can be used as additional descriptors. In this chapter, the authors present theoretically motivated QSAR algorithms that can automatically identify informative subgraph patterns. A graph mining subroutine is embedded in the mother algorithm and it is called repeatedly to collect patterns progressively. The authors present three variations that build on support vector machines (SVM), partial least squares regression (PLS) and least angle regression (LARS). In comparison to graph kernels, our methods are more interpretable, thereby allows chemists to identify salient subgraph features to improve the druglikeliness of lead compounds. © 2011, IGI Global.

リンク情報
DOI
https://doi.org/10.4018/978-1-61520-911-8.ch006
ID情報
  • DOI : 10.4018/978-1-61520-911-8.ch006
  • SCOPUS ID : 84862850892

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