The joint use of multiple drugs may cause unintended drug-drug interactions (DDIs) and result in adverse consequence to the patients. Accurate identification of DDI types can not only provide hints to avoid these accidental events, but also elaborate the underlying mechanisms by how DDIs occur. Several computational methods have been proposed for multi-type DDI prediction, but room remains for improvement in prediction performance. In this study, we propose a supervised contrastive learning based method, MDDI-SCL, implemented by three-level loss functions, to predict multi-type DDIs. MDDI-SCL is mainly composed of three modules: drug feature encoder and mean squared error loss module, drug latent feature fusion and supervised contrastive loss module, multi-type DDI prediction and classification loss module. The drug feature encoder and mean squared error loss module uses self-attention mechanism and autoencoder to learn drug-level latent features. The drug latent feature fusion and supervised contrastive loss module uses multi-scale feature fusion to learn drug pair-level latent features. The prediction and classification loss module predicts DDI types of each drug pair. We evaluate MDDI-SCL on three different tasks of two datasets. Experimental results demonstrate that MDDI-SCL achieves better or comparable performance as the state-of-the-art methods. Furthermore, the effectiveness of supervised contrastive learning is validated by ablation experiment, and the feasibility of MDDI-SCL is supported by case studies. The source codes are available at https://github.com/ShenggengLin/MDDI-SCL .

中文翻译：

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MDDI-SCL：通过有监督的对比学习预测多种药物相互作用

联合使用多种药物可能会导致意想不到的药物相互作用 (DDI)，并对患者造成不良后果。准确识别 DDI 类型不仅可以提供避免这些意外事件的提示，还可以通过 DDI 的发生方式详细说明底层机制。已经为多类型 DDI 预测提出了几种计算方法，但预测性能仍有改进的空间。在这项研究中，我们提出了一种基于监督对比学习的方法，MDDI-SCL，由三级损失函数实现，以预测多类型 DDI。MDDI-SCL主要由三个模块组成：药物特征编码器和均方误差损失模块、药物潜在特征融合和监督对比损失模块、多类型DDI预测和分类损失模块。药物特征编码器和均方误差损失模块使用自注意力机制和自动编码器来学习药物级潜在特征。药物潜在特征融合和监督对比损失模块使用多尺度特征融合来学习药物对级潜在特征。预测和分类损失模块预测每个药物对的 DDI 类型。我们在两个数据集的三个不同任务上评估 MDDI-SCL。实验结果表明，MDDI-SCL 与最先进的方法相比具有更好或相当的性能。此外，消融实验验证了监督对比学习的有效性，案例研究支持了 MDDI-SCL 的可行性。源代码可在 https://github.com/ShenggengLin/MDDI-SCL 获得。