DDI-GCN：基于可解释图卷积网络的药物-药物相互作用预测。

DDI-GCN: Drug-drug interaction prediction via explainable graph convolutional networks.

机构信息

The Center for Big Data Research in Burns and Trauma, College of Computer and Data Science/College of Software, Fuzhou University, Fujian Province, China.

Department of Plastic Surgery, the First Affiliated Hospital of Fujian Medical University, Fuzhou, China.

出版信息

Artif Intell Med. 2023 Oct;144:102640. doi: 10.1016/j.artmed.2023.102640. Epub 2023 Aug 21.

DOI:10.1016/j.artmed.2023.102640

PMID:37783544

Abstract

Drug-drug interactions (DDI) may lead to unexpected side effects, which is a growing concern in both academia and industry. Many DDIs have been reported, but the underlying mechanisms are not well understood. Predicting and understanding DDIs can help researchers to improve drug safety and protect patient health. Here, we introduce DDI-GCN, a method that utilizes graph convolutional networks (GCN) to predict DDIs based on chemical structures. We demonstrate that this method achieves state-of-the-art prediction performance on the independent hold-out set. It can also provide visualization of structural features associated with DDIs, which can help us to study the underlying mechanisms. To make it easy and accessible to use, we developed a web server for DDI-GCN, which is freely available at http://wengzq-lab.cn/ddi/.

摘要

药物-药物相互作用（DDI）可能导致意想不到的副作用，这在学术界和工业界都是一个日益关注的问题。已经报道了许多 DDI，但潜在机制尚不清楚。预测和理解 DDI 可以帮助研究人员提高药物安全性并保护患者健康。在这里，我们介绍了 DDI-GCN，这是一种利用图卷积网络（GCN）根据化学结构预测 DDI 的方法。我们证明，该方法在独立的保留集上达到了最先进的预测性能。它还可以提供与 DDI 相关的结构特征的可视化，这有助于我们研究潜在机制。为了使其易于使用和访问，我们开发了一个用于 DDI-GCN 的网络服务器，可在 http://wengzq-lab.cn/ddi/ 免费获得。