Drug-drug interaction prediction based on co-medication patterns and graph matching

Wen Hao Chiang, Li Shen, Lang Li, Xia Ning

Research output: Contribution to journalArticlepeer-review


High-order drug-drug interactions (DDIs) and associated adverse drug reactions (ADRs) are common, particularly for elderly people, and therefore represent a significant public health problem. In this paper, the problem of predicting whether a drug combination of arbitrary orders is likely to induce adverse drug reactions is considered. To solve this problem, novel kernels over drug combinations of arbitrary orders are developed within support vector machines (SVMs) for the prediction. Graph matching methods are used in the novel kernels to measure the similarities among drug combinations, in which drug co-medication patterns are leveraged to measure single drug similarities. The experimental results on a real-world dataset demonstrated that the new kernels achieve an area under the curve (AUC) value 0.912 for the prediction problem. The new methods with drug co-medication based single drug similarities can accurately predict whether a drug combination is likely to induce adverse drug reactions of interest.

Original languageEnglish (US)
Pages (from-to)36-57
Number of pages22
JournalInternational Journal of Computational Biology and Drug Design
Issue number1
StatePublished - 2020
Externally publishedYes


  • Adverse drug reaction
  • Arbitrary order
  • Binary classification problem
  • Comedication
  • Drug combination similarity
  • Drug-drug interaction prediction
  • Graph matching
  • Myopathy
  • SVMs
  • Single drug similarity
  • Support vector machines

ASJC Scopus subject areas

  • Computer Science Applications
  • Drug Discovery

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