Background: Drug synergy occurs when the combined effect of two drugs is
greater than the sum of the individual drugs' effect. While cell line data
measuring the effect of single drugs are readily available, there is relatively
less comparable data on drug synergy given the vast amount of possible drug
combinations. Thus, there is interest to use computational approaches to
predict drug synergy for untested pairs of drugs.
Methods: We introduce a Graph Neural Network (GNN) based model for drug
synergy prediction, which utilizes drug chemical structures and cell line gene
expression data. We use information from the largest drug combination database
available (DrugComb), combining drug synergy scores in order to construct high
confidence benchmark datasets.
Results: Our proposed solution for drug synergy predictions offers a number
of benefits: 1) It utilizes a combination of 34 distinct drug synergy datasets
to learn on a wide variety of drugs and cell lines representations. 2) It is
trained on constructed high confidence benchmark datasets. 3) It learns
task-specific drug representations, instead of relying on generalized and
pre-computed chemical drug features. 4) It achieves similar or better
prediction performance (AUPR scores ranging from 0.777 to 0.964) compared to
state-of-the-art baseline models when tested on various benchmark datasets.
Conclusions: We demonstrate that a GNN based model can provide
state-of-the-art drug synergy predictions by learning task-specific
representations of drugs