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  3. Machine Learning For Predicting Human Drug-induced Cardiotoxicity: A Scoping Review.
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  3. Machine Learning For Predicting Human Drug-induced Cardiotoxicity: A Scoping Review.

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Machine Learning for Predicting Human Drug-Induced Cardiotoxicity: A Scoping Review.

Ja-Young Han1, Min Jung Kim1, Hyunwoo Kim2

  • 1Graduate School of Pharmaceutical Sciences, Ewha Womans University, Seoul 03760, Republic of Korea.

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|December 24, 2025

View abstract on PubMed

Summary
This summary is machine-generated.

Machine learning models show promise in predicting diverse drug-induced cardiotoxicity outcomes beyond hERG inhibition. Rigorous validation and heterogeneous data integration are crucial for improving predictive accuracy in drug safety.

Keywords:
cardiotoxicitymachine learningprediction modelscoping review

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Area of Science:

  • Pharmacology and Toxicology
  • Computational Biology
  • Drug Development

Background:

  • Drug-induced cardiotoxicity is a significant hurdle in pharmaceutical research and patient safety.
  • Existing machine learning (ML) approaches often focus narrowly on specific mechanisms like hERG inhibition.
  • A broader predictive scope is needed for comprehensive cardiotoxicity assessment.

Purpose of the Study:

  • To systematically review studies utilizing ML models for predicting a wide spectrum of drug-induced cardiotoxicity.
  • To identify common data sources, features, algorithms, and performance metrics in this field.
  • To assess the current state and potential of ML in drug cardiotoxicity prediction.

Main Methods:

  • Systematic literature search across major scientific databases (PubMed, EMBASE, SCOPUS, Web of Science).
  • Extraction and categorization of data including study sources, feature types, ML algorithms, and performance evaluation methods.
  • Analysis of 25 selected studies meeting inclusion criteria for ML-based cardiotoxicity prediction.

    • Main Results:

    • Studies covered diverse cardiotoxicity outcomes like arrhythmia, cardiac failure, and myocardial infarction.
    • SIDER database and molecular descriptors were common data sources and features.
    • Support Vector Machines (SVM) and Random Forest (RF) were frequently used, demonstrating promising predictive performance with AUC-ROC > 0.70 and accuracy > 0.75 in several cases.
    • External validation was limited but showed ML's potential.

    Conclusions:

    • Machine learning holds substantial promise for predicting various drug-induced cardiotoxicity.
    • Integrating diverse data types and employing robust validation strategies are key to enhancing predictive models.
    • Further research is needed to address limitations in external validation and improve model generalizability.