Drug synergy prediction using heterogeneous stacking ensemble learning

Malignant diseases are considered the most prominent and widespread causes of death affecting populations globally. Synergistic drug combinations have shown beneficial therapeutic results in the treatment of malignant diseases. Although techniques such as clinical trials and high-throughput drug screening are commonly used to discover promising synergistic drug pairs, they are time-consuming and expensive. Over the past years, various AI-based drug synergy techniques including machine learning and deep learning have been utilized in finding synergistic drug combinations. Individually using these methods for synergy prediction has the disadvantages of overfitting and lack of interpretability. Combining different AI methods through ensemble learning provides better predictions by more closely representing the underlying distribution of data. This study utilized the heterogeneous stacking ensemble approach (HTeSyn) by aggregating four machine learning methods as base learners and one neural method as meta-learner.This multi-faceted approach helps in correcting classification results and provides more reliable synergy predictions, which is crucial for identifying effective drug combinations. For the bliss independence synergy task, HTeSyn outperforms the state-of-the-art synergy prediction method with an accuracy of 94%, RMSE of 12.5, and r² of 0.8.