MMRCL: An interpretable multi-modal deep learning framework for predicting hERG blockers

Puntos clave

• The framework predicts hERG blockers with high accuracy, enhancing drug safety assessments.
• Key performance metrics highlight an accuracy of over 85% in identifying hERG blockers.
• Assessment using a multi-modal deep learning model leverages diverse data types for improved predictions.
• Highlights the importance of interpretability in predictive models, ensuring transparency in findings.