  Drug synergy arises when the combined impact of two drugs exceeds the sum of
their individual effects. While single-drug effects on cell lines are
well-documented, the scarcity of data on drug synergy, considering the vast
array of potential drug combinations, prompts a growing interest in
computational approaches for predicting synergies in untested drug pairs. We
introduce a Graph Neural Network (\textit{GNN}) based model for drug synergy
prediction, which utilizes drug chemical structures and cell line gene
expression data. We extract data from the largest available drug combination
database (DrugComb) and generate multiple synergy scores (commonly used in the
literature) to create seven datasets that serve as a reliable benchmark with
high confidence. In contrast to conventional models relying on pre-computed
chemical features, our GNN-based approach learns task-specific drug
representations directly from the graph structure of the drugs, providing
superior performance in predicting drug synergies. Our work suggests that
learning task-specific drug representations and leveraging a diverse dataset is
a promising approach to advancing our understanding of drug-drug interaction
and synergy.
