An algorithmic framework for predicting side-effects of drugs

Nir Atias*, Roded Sharan

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


One of the critical stages in drug development is the identification of potential side effects for promising drug leads. Large scale clinical experiments aimed at discovering such side effects are very costly and may miss subtle or rare side effects. To date, and to the best of our knowledge, no computational approach was suggested to systematically tackle this challenge. In this work we report on a novel approach to predict the side effects of a given drug. Starting from a query drug, a combination of canonical correlation analysis and network-based diffusion are applied to predict its side effects. We evaluate our method by measuring its performance in cross validation using a comprehensive data set of 692 drugs and their known side effects derived from package inserts. For 34% of the drugs the top scoring side effect matches a known side effect of the drug. Remarkably, even on unseen data, our method is able to infer side effects that highly match existing knowledge. Our method thus represents a promising first step toward shortcutting the process and reducing the cost of side effect elucidation.

Original languageEnglish
Title of host publicationResearch in Computational Molecular Biology - 14th Annual International Conference, RECOMB 2010, Proceedings
Number of pages14
StatePublished - 2010
Event14th Annual International Conference on Research in Computational Molecular Biology, RECOMB 2010 - Lisbon, Portugal
Duration: 25 Apr 201028 Apr 2010

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume6044 LNBI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference14th Annual International Conference on Research in Computational Molecular Biology, RECOMB 2010


  • Canonical correclation analysis
  • Drug targets
  • Network diffusion
  • Prediction


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