Review of Predicting Synergistic Drug Combinations

Yichen Pan, Haotian Ren, Liang Lan, Yixue Li*, Tao Huang*

*Corresponding author for this work

Research output: Contribution to journalReview articlepeer-review

2 Citations (Scopus)


The prediction of drug combinations is of great clinical significance. In many diseases, such as high blood pressure, diabetes, and stomach ulcers, the simultaneous use of two or more drugs has shown clear efficacy. It has greatly reduced the progression of drug resistance. This review presents the latest applications of methods for predicting the effects of drug combinations and the bioactivity databases commonly used in drug combination prediction. These studies have played a significant role in developing precision therapy. We first describe the concept of synergy. we study various publicly available databases for drug combination prediction tasks. Next, we introduce five algorithms applied to drug combinatorial prediction, which include traditional machine learning methods, deep learning methods, mathematical methods, systems biology methods and search algorithms. In the end, we sum up the difficulties encountered in prediction models.

Original languageEnglish
Article number1878
Issue number9
Publication statusPublished - Sept 2023

Scopus Subject Areas

  • Ecology, Evolution, Behavior and Systematics
  • Biochemistry, Genetics and Molecular Biology(all)
  • Space and Planetary Science
  • Palaeontology

User-Defined Keywords

  • deep learning
  • drug combination
  • drug resistance
  • machine learning
  • side effects
  • synergistic effect


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