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

ABSTRACT:

Background

Drug-induced cardiotoxicity poses a major challenge in drug development and clinical safety. Although machine learning (ML) methods have shown potential in predicting cardiotoxic risks, prior research has largely focused on specific mechanisms such as human Ether-à-go-go-Related Gene (hERG) inhibition. This scoping review systematically examined studies applying ML models to predict a broad range of drug-induced cardiotoxicity outcomes.

Methods

A systematic search of PubMed, EMBASE, SCOPUS, and Web of Science identified studies developing ML models for cardiotoxicity prediction. Extracted data included sources, feature types, algorithms, and performance metrics, categorized by evaluation method (training, testing, cross-validation, or external validation).

Resu

SUBMITTER: Han JY 

PROVIDER: S-EPMC12737777 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature