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Related Experiment Video

Updated: Jul 8, 2025

A Data Integration Workflow to Identify Drug Combinations Targeting Synthetic Lethal Interactions
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Drug-drug interaction prediction: databases, web servers and computational models.

Yan Zhao1, Jun Yin1, Li Zhang1

  • 1School of Information and Control Engineering, China University of Mining and Technology, Xuzhou 221116, China.

Briefings in Bioinformatics
|December 19, 2023
PubMed
Summary

Understanding drug-drug interactions (DDIs) is crucial for effective combination therapy. This review explores computational models for predicting DDIs, aiming to enhance efficacy and reduce adverse reactions in clinical treatments.

Keywords:
computational modeldatabasedrugdrug–drug interactionweb server

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Area of Science:

  • Pharmacology
  • Computational Biology
  • Bioinformatics

Background:

  • Drug combinations are vital in clinical practice for improving therapeutic outcomes and minimizing side effects.
  • However, improper drug combinations can lead to reduced efficacy and adverse reactions, necessitating a thorough understanding of drug-drug interactions (DDIs).

Approach:

  • This review introduces the fundamental concepts and classifications of DDIs.
  • It describes key publicly available databases and web servers for experimentally verified or predicted DDIs.
  • The review summarizes three main types of computational models for DDI prediction: traditional machine learning, deep learning, and score function-based models.

Key Points:

  • Computational models offer an efficient auxiliary tool for predicting DDIs, reducing experimental costs and guiding combination therapy.
  • Advantages and limitations of various prediction models, including machine learning and deep learning approaches, are discussed.
  • The review highlights current challenges in DDI prediction research and proposes future research directions.

Conclusions:

  • Accurate prediction of DDIs is essential for optimizing drug combination therapies.
  • Further research is needed to address existing limitations in DDI prediction models.
  • Developing robust computational tools will significantly aid in the safe and effective use of drug combinations in medicine.