Prediction of Drug Synergy by Ensemble Learning
This work addresses the challenge of identifying effective drug combinations for complex diseases like cancer, but it is incremental as it builds on existing machine learning approaches in the field.
The study tackled the problem of predicting drug synergy for combinational cancer therapy by investigating different compound representations and proposing an ensemble method, which outperformed baseline models on a large drug combination screen dataset.
One of the promising methods for the treatment of complex diseases such as cancer is combinational therapy. Due to the combinatorial complexity, machine learning models can be useful in this field, where significant improvements have recently been achieved in determination of synergistic combinations. In this study, we investigate the effectiveness of different compound representations in predicting the drug synergy. On a large drug combination screen dataset, we first demonstrate the use of a promising representation that has not been used for this problem before, then we propose an ensemble on representation-model combinations that outperform each of the baseline models.