Combining drug and gene similarity measures for drug-target elucidation

Liat Perlman, Assaf Gottlieb, Nir Atias, Eytan Ruppin, Roded Sharan*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review


Understanding drugs and their modes of action is a fundamental challenge in systems medicine. Key to addressing this challenge is the elucidation of drug targets, an important step in the search for new drugs or novel targets for existing drugs. Incorporating multiple biological information sources is of essence for improving the accuracy of drug target prediction. In this article, we introduce a novel framework-Similarity-based Inference of drug-TARgets (SITAR)-for incorporating multiple drug-drug and gene-gene similarity measures for drug target prediction. The framework consists of a new scoring scheme for drug-gene associations based on a given pair of drug-drug and gene-gene similarity measures, combined with a logistic regression component that integrates the scores of multiple measures to yield the final association score. We apply our framework to predict targets for hundreds of drugs using both commonly used and novel drug-drug and gene-gene similarity measures and compare our results to existing state of the art methods, markedly outperforming them. We then employ our framework to make novel target predictions for hundreds of drugs; we validate these predictions via curated databases that were not used in the learning stage. Our framework provides an extensible platform for incorporating additional emerging similarity measures among drugs and genes. Supplementary Material is available at

Original languageEnglish
Pages (from-to)133-145
Number of pages13
JournalJournal of Computational Biology
Issue number2
StatePublished - 1 Feb 2011


  • computational molecular biology
  • gene expression
  • gene networks
  • genetic variation
  • machine learning
  • sequence analysis


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