Prediction of Drug Synergy by Ensemble Learning

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. 1 Scientific Background A drug combination is called synergistic if the effect of the drug combination on the reference cell is greater than the total effect taken from the administration of the individual drugs. If the opposite situation is observed, the drug combination is called antagonistic . Understanding whether a combination is antagonistic or synergistic is a resource and time intensive task.