Compound analysis via graph kernels incorporating chirality.

@article{Brown2010CompoundAV,
  title={Compound analysis via graph kernels incorporating chirality.},
  author={J. B. Brown and Takashi Urata and Takeyuki Tamura and Midori A Arai and Takeo Kawabata and Tatsuya Akutsu},
  journal={Journal of bioinformatics and computational biology},
  year={2010},
  volume={8 Suppl 1},
  pages={
          63-81
        }
}
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… CONTINUE READING