背景:药物-药物相互作用事件影响药物组合的有效性,并可能导致意想不到的副作用或加剧潜在疾病,危及患者预后。大多数现有方法仅限于预测两种药物是否相互作用或药物相互作用的类型。虽然很少有研究试图预测药物组合副作用的具体风险水平。
方法:在本研究中,我们提议MathEagle,一种基于多头注意力和异质属性图学习的药物组合风险预测方法。最初,我们将药物和药物之间的三种不同风险水平建模为异质信息图.随后,通过消息传递神经网络和图嵌入算法利用药物的行为和化学结构特征,分别。最终,MathEagle采用异构图卷积和多头注意力机制来学习药物节点的有效潜在表示,并以端到端方式估计成对药物的风险水平。
结果:为了评估模型的有效性和鲁棒性,五折交叉验证,消融实验,并进行了案例研究。MathEagle在药物风险水平预测任务上实现了85.85%的准确度和0.9701的AUC,并且优于所有比较模型。MathEagle预测器可在http://120.77.11.78/MathEagle/免费访问。
结论:实验结果表明,MathEagle可以作为预测药物组合的准确风险的有效工具,协助指导临床用药,并提高患者的治疗效果。
BACKGROUND: Drug-drug interaction events influence the effectiveness of drug combinations and can lead to unexpected side effects or exacerbate underlying diseases, jeopardizing patient prognosis. Most existing methods are restricted to predicting whether two drugs interact or the type of drug-drug interactions, while very few studies endeavor to predict the specific risk levels of side effects of drug combinations.
METHODS: In this study, we propose MathEagle, a novel approach to predict accurate risk levels of drug combinations based on multi-head attention and heterogeneous attribute graph learning. Initially, we model drugs and three distinct risk levels between drugs as a heterogeneous information graph. Subsequently, behavioral and chemical structure features of drugs are utilized by message passing neural networks and graph embedding algorithms, respectively. Ultimately, MathEagle employs heterogeneous graph convolution and multi-head attention mechanisms to learn efficient latent representations of drug nodes and estimates the risk levels of pairwise drugs in an end-to-end manner.
RESULTS: To assess the effectiveness and robustness of the model, five-fold cross-validation, ablation experiments, and case studies were conducted. MathEagle achieved an accuracy of 85.85 % and an AUC of 0.9701 on the drug risk level prediction task and is superior to all comparative models. The MathEagle predictor is freely accessible at http://120.77.11.78/MathEagle/.
CONCLUSIONS: The experimental results indicate that MathEagle can function as an effective tool for predicting accurate risk of drug combinations, aiding in guiding clinical medication, and enhancing patient outcomes.