Tri-graph Information Propagation for Polypharmacy Side Effect Prediction

Modeling polypharmacy side effects with graph convolutional networks

The use of drug combinations, termed polypharmacy, is common to treat patients with complex diseases and co-existing conditions. However, a major consequence of polypharmacy is a much higher risk of adverse side effects for the patient. Polypharmacy side effects emerge because of drug-drug interactions, in which activity of one drug may change if taken with another drug. The knowledge of drug interactions is limited because these complex relationships are rare, and are usually not observed in relatively small clinical testing. Discovering polypharmacy side effects thus remains an important cha…

Knowledge Graph Completion to Predict Polypharmacy Side Effects

The polypharmacy side effect prediction problem considers cases in which two drugs taken individually do not result in a particular side effect; however, when the two drugs are taken in combination, the side effect manifests. In this work, we demonstrate that multi-relational knowledge graph completion achieves state-of-the-art results on the polypharmacy side effect prediction problem. Empirical results show that our approach is particularly effective when the protein targets of the drugs are well-characterized. In contrast to prior work, our approach provides more interpretable predictions a…

基于张量分解的多药副作用快速预测

Fast Polypharmacy Side Effect Prediction Using Tensor Factorisation

本研究针对多药副作用预测难题，优化张量分解模型SimplE，实现了兼具当前最优精度与极快训练速度的预测效果，同时验证了单药数据的最优融入方式。

药物相互作用向量神经网络：DrIVeNN

Drug Interaction Vectors Neural Network: DrIVeNN

针对多药治疗中药物不良事件预测难题，提出整合多类药物特征的两层神经网络DrIVeNN，经实验验证其性能优于现有最优模型，且心血管疾病特定领域模型预测准确率提升显著。

Drug-Drug Adverse Effect Prediction with Graph Co-Attention

Complex or co-existing diseases are commonly treated using drug combinations, which can lead to higher risk of adverse side effects. The detection of polypharmacy side effects is usually done in Phase IV clinical trials, but there are still plenty which remain undiscovered when the drugs are put on the market. Such accidents have been affecting an increasing proportion of the population (15% in the US now) and it is thus of high interest to be able to predict the potential side effects as early as possible. Systematic combinatorial screening of possible drug-drug interactions (DDI) is challeng…

DDI Prediction via Heterogeneous Graph Attention Networks

Polypharmacy, defined as the use of multiple drugs together, is a standard treatment method, especially for severe and chronic diseases. However, using multiple drugs together may cause interactions between drugs. Drug-drug interaction (DDI) is the activity that occurs when the impact of one drug changes when combined with another. DDIs may obstruct, increase, or decrease the intended effect of either drug or, in the worst-case scenario, create adverse side effects. While it is critical to detect DDIs on time, it is timeconsuming and expensive to identify them in clinical trials due to their s…