PDF(Pubmed)
Abstract：
BACKGROUND: Accurately identifying the risk level of drug combinations is of great significance in investigating the mechanisms of combination medication and adverse reactions. Most existing methods can only predict whether there is an interaction between two drugs, but cannot directly determine their accurate risk level.
METHODS: In this study, we propose a multi-class drug combination risk prediction model named AERGCN-DDI, utilizing a relational graph convolutional network with a multi-head attention mechanism. Drug-drug interaction events with varying risk levels are modeled as a heterogeneous information graph. Attribute features of drug nodes and links are learned based on compound chemical structure information. Finally, the AERGCN-DDI model is proposed to predict drug combination risk level based on heterogenous graph neural network and multi-head attention modules.
RESULTS: To evaluate the effectiveness of the proposed method, five-fold cross-validation and ablation study were conducted. Furthermore, we compared its predictive performance with baseline models and other state-of-the-art methods on two benchmark datasets. Empirical studies demonstrated the superior performances of AERGCN-DDI.
CONCLUSIONS: AERGCN-DDI emerges as a valuable tool for predicting the risk levels of drug combinations, thereby aiding in clinical medication decision-making, mitigating severe drug side effects, and enhancing patient clinical prognosis.
METHODS: In this study, we propose a multi-class drug combination risk prediction model named AERGCN-DDI, utilizing a relational graph convolutional network with a multi-head attention mechanism. Drug-drug interaction events with varying risk levels are modeled as a heterogeneous information graph. Attribute features of drug nodes and links are learned based on compound chemical structure information. Finally, the AERGCN-DDI model is proposed to predict drug combination risk level based on heterogenous graph neural network and multi-head attention modules.
RESULTS: To evaluate the effectiveness of the proposed method, five-fold cross-validation and ablation study were conducted. Furthermore, we compared its predictive performance with baseline models and other state-of-the-art methods on two benchmark datasets. Empirical studies demonstrated the superior performances of AERGCN-DDI.
CONCLUSIONS: AERGCN-DDI emerges as a valuable tool for predicting the risk levels of drug combinations, thereby aiding in clinical medication decision-making, mitigating severe drug side effects, and enhancing patient clinical prognosis.
摘要：
背景：准确识别联合用药的风险水平对于研究联合用药的机制和不良反应具有重要意义。大多数现有方法只能预测两种药物之间是否存在相互作用，但不能直接确定其准确的风险水平。
方法：在本研究中，我们提出了一种名为AERGCN-DDI的多类药物组合风险预测模型，利用具有多头注意机制的关系图卷积网络。具有不同风险水平的药物-药物相互作用事件被建模为异构信息图。基于化合物化学结构信息学习药物节点和链接的属性特征。最后,提出了基于异构图神经网络和多头注意模块的AERGCN-DDI模型预测药物组合风险水平。
结果：为了评估所提出方法的有效性，我们进行了5倍交叉验证和消融研究.此外，我们将其预测性能与基线模型和其他最先进的方法在两个基准数据集上进行了比较.实证研究证明了AERGCN-DDI的优异性能。
结论：AERGCN-DDI成为预测药物组合风险水平的有价值的工具，从而帮助临床用药决策，减轻严重的药物副作用，提高患者的临床预后。
方法：在本研究中，我们提出了一种名为AERGCN-DDI的多类药物组合风险预测模型，利用具有多头注意机制的关系图卷积网络。具有不同风险水平的药物-药物相互作用事件被建模为异构信息图。基于化合物化学结构信息学习药物节点和链接的属性特征。最后,提出了基于异构图神经网络和多头注意模块的AERGCN-DDI模型预测药物组合风险水平。
结果：为了评估所提出方法的有效性，我们进行了5倍交叉验证和消融研究.此外，我们将其预测性能与基线模型和其他最先进的方法在两个基准数据集上进行了比较.实证研究证明了AERGCN-DDI的优异性能。
结论：AERGCN-DDI成为预测药物组合风险水平的有价值的工具，从而帮助临床用药决策，减轻严重的药物副作用，提高患者的临床预后。
