Structured Output Prediction of Anti-cancer Drug Activity

  • Hongyu Su
  • Markus Heinonen
  • Juho Rousu
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6282)


We present a structured output prediction approach for classifying potential anti-cancer drugs. Our QSAR model takes as input a description of a molecule and predicts the activity against a set of cancer cell lines in one shot. Statistical dependencies between the cell lines are encoded by a Markov network that has cell lines as nodes and edges represent similarity according to an auxiliary dataset. Molecules are represented via kernels based on molecular graphs. Margin-based learning is applied to separate correct multilabels from incorrect ones. The performance of the multilabel classification method is shown in our experiments with NCI-Cancer data containing the cancer inhibition potential of drug-like molecules against 59 cancer cell lines. In the experiments, our method outperforms the state-of-the-art SVM method.


Support Vector Machine Inductive Logic Programming Support Vector Machine Method Markov Network Graph Kernel 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Hongyu Su
    • 1
  • Markus Heinonen
    • 1
  • Juho Rousu
    • 1
  1. 1.Department of Computer ScienceUniversity of HelsinkiFinland

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