Polypharmacy refers to the administration of multiple drugs on a daily basis. It has demonstrated effectiveness in treating many complex diseases , but it has a higher risk of adverse drug reactions. Hence, the prediction of polypharmacy side effects is an essential step in drug testing, especially for new drugs. This paper shows that the current knowledge graph (KG) based state-of-the-art approach to polypharmacy side effect prediction does not work well for new drugs, as they have a low number of known connections in the KG. We propose a new method , SimVec, that solves this problem by enhancing the KG structure with a structure-aware node initialization and weighted drug similarity edges. We also devise a new 3-step learning process, which iteratively updates node embeddings related to side effects edges, similarity edges, and drugs with limited knowledge. Our model significantly outperforms existing KG-based models. Additionally, we examine the problem of negative relations generation and show that the cache-based approach works best for polypharmacy tasks.

中文翻译：

---

SimVec：预测新药的多药副作用

多种药物是指每天服用多种药物。它在治疗许多复杂疾病方面已被证明有效，但它具有较高的药物不良反应风险。因此，预测多种药物的副作用是药物测试的重要步骤，尤其是对于新药。本文表明，当前基于知识图 (KG) 的最先进的多药副作用预测方法不适用于新药，因为它们在 KG 中的已知连接数量很少。我们提出了一种新方法 SimVec，它通过使用结构感知节点初始化和加权药物相似性边缘增强 KG 结构来解决这个问题。我们还设计了一个新的 3 步学习过程，它迭代地更新与副作用边、相似边、和知识有限的药物。我们的模型明显优于现有的基于 KG 的模型。此外，我们检查了负关系生成的问题，并表明基于缓存的方法最适合多药任务。