Graph mining in chemoinformatics

Hiroto Saigo, Koji Tsuda

研究成果: Chapter in Book/Report/Conference proceedingChapter

3 被引用数 (Scopus)

抄録

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.

本文言語英語
ホスト出版物のタイトルChemoinformatics and Advanced Machine Learning Perspectives
ホスト出版物のサブタイトルComplex Computational Methods and Collaborative Techniques
出版社IGI Global
ページ95-128
ページ数34
ISBN(印刷版)9781615209118
DOI
出版ステータス出版済み - 12 1 2010
外部発表はい

All Science Journal Classification (ASJC) codes

  • 農業および生物科学(全般)

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