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 is trained on high confidence benchmark dataset. 2) It utilizes 34 distinct drug synergy datasets to learn on a wide variety of drugs and cell lines representations. 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.
翻译:药物协同效应:两种药物的综合效应大于个别药物效应的总和时,就会产生药物协同效应。虽然衡量单一药物效应的细胞线数据随时可得,但鉴于可能的药物组合量巨大,关于药物协同效应的可比数据相对较少。因此,人们有兴趣使用计算方法预测未经测试的药物配对的药物协同效应。方法:我们采用基于图表神经网络(GNN)的药物协同预测模型,该模型使用药物化学结构和细胞细胞细胞表示法数据。我们使用现有最大的药物综合数据库(DrugComb)的信息,将药物协同得分结合起来,以建立高信任基准数据集。结果:我们拟议的药物协同效应预测方案提供了若干好处:1)它接受过高信任基准数据集的培训。 2)它利用34个不同的药物协同效应数据集学习多种药物和细胞线表示法。 3)它学习具体任务药物表示法,而不是依赖普遍和预先计算过的化学药物表示法特征。4)它实现类似或更好的预测业绩(AUPR的得分,从0.77到0.964不等,以建立高信任基准数据集为基础,我们通过测试了各种基准模型,可以提供州基准模型。我们通过测试了各种基准模型,可以提供各种基准模型。