Abstract
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.
Original language | English |
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Pages (from-to) | 63-81 |
Number of pages | 19 |
Journal | Journal of Bioinformatics and Computational Biology |
Volume | 8 |
Issue number | SUPPL. 1 |
DOIs | |
Publication status | Published - 2010 Dec |
Externally published | Yes |
Keywords
- Kernel method
- QSAR
- QSPR
- graph kernel
- support vector machine
ASJC Scopus subject areas
- Biochemistry
- Molecular Biology
- Computer Science Applications