DDoS: A Graph Neural Network based Drug Synergy Prediction Algorithm

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