Large-scale exploration and analysis of drug combinations

Peng Li, Chao Huang, Yingxue Fu, Jinan Wang, Ziyin Wu, Jinlong Ru, Chunli Zheng, Zihu Guo, Xuetong Chen, Wei Zhou, Wenjuan Zhang, Yan Li, Jianxin Chen, Aiping LYU, Yonghua Wang*

*Corresponding author for this work

Research output: Contribution to journalJournal articlepeer-review

123 Citations (Scopus)


Motivation: Drug combinations are a promising strategy for combating complex diseases by improving the efficacy and reducing corresponding side effects. Currently, a widely studied problem in pharmacology is to predict effective drug combinations, either through empirically screening in clinic or pure experimental trials. However, the large-scale prediction of drug combination by a systems method is rarely considered. Results: We report a systems pharmacology framework to predict drug combinations (PreDCs) on a computational model, termed probability ensemble approach (PEA), for analysis of both the efficacy and adverse effects of drug combinations. First, a Bayesian network integrating with a similarity algorithm is developed to model the combinations from drug molecular and pharmacological phenotypes, and the predictions are then assessed with both clinical efficacy and adverse effects. It is illustrated that PEA can predict the combination efficacy of drugs spanning different therapeutic classes with high specificity and sensitivity (AUC∈=∈0.90), which was further validated by independent data or new experimental assays. PEA also evaluates the adverse effects (AUC∈=∈0.95) quantitatively and detects the therapeutic indications for drug combinations. Finally, the PreDC database includes 1571 known and 3269 predicted optimal combinations as well as their potential side effects and therapeutic indications.

Original languageEnglish
Pages (from-to)2007-2016
Number of pages10
Issue number12
Publication statusPublished - 15 Jun 2015

Scopus Subject Areas

  • Statistics and Probability
  • Biochemistry
  • Molecular Biology
  • Computer Science Applications
  • Computational Theory and Mathematics
  • Computational Mathematics


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