Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, Urumqi, 830011, China.
University of Chinese Academy of Sciences, Beijing, 100049, China.
Brief Bioinform. 2022 Jan 17;23(1). doi: 10.1093/bib/bbab526.
The outbreak of COVID-19 caused by SARS-coronavirus (CoV)-2 has made millions of deaths since 2019. Although a variety of computational methods have been proposed to repurpose drugs for treating SARS-CoV-2 infections, it is still a challenging task for new viruses, as there are no verified virus-drug associations (VDAs) between them and existing drugs. To efficiently solve the cold-start problem posed by new viruses, a novel constrained multi-view nonnegative matrix factorization (CMNMF) model is designed by jointly utilizing multiple sources of biological information. With the CMNMF model, the similarities of drugs and viruses can be preserved from their own perspectives when they are projected onto a unified latent feature space. Based on the CMNMF model, we propose a deep learning method, namely VDA-DLCMNMF, for repurposing drugs against new viruses. VDA-DLCMNMF first initializes the node representations of drugs and viruses with their corresponding latent feature vectors to avoid a random initialization and then applies graph convolutional network to optimize their representations. Given an arbitrary drug, its probability of being associated with a new virus is computed according to their representations. To evaluate the performance of VDA-DLCMNMF, we have conducted a series of experiments on three VDA datasets created for SARS-CoV-2. Experimental results demonstrate that the promising prediction accuracy of VDA-DLCMNMF. Moreover, incorporating the CMNMF model into deep learning gains new insight into the drug repurposing for SARS-CoV-2, as the results of molecular docking experiments reveal that four antiviral drugs identified by VDA-DLCMNMF have the potential ability to treat SARS-CoV-2 infections.
自 2019 年以来，由严重急性呼吸系统综合征冠状病毒 2（SARS-CoV-2）引起的 COVID-19 大流行已导致数百万人死亡。尽管已经提出了多种计算方法来重新利用药物治疗 SARS-CoV-2 感染，但对于新病毒来说，这仍然是一项具有挑战性的任务，因为它们与现有药物之间没有经过验证的病毒-药物关联（VDA）。为了有效地解决新病毒带来的冷启动问题，我们设计了一种新颖的约束多视图非负矩阵分解（CMNMF）模型，该模型联合利用多种生物信息源。使用 CMNMF 模型，当药物和病毒分别被投影到统一的潜在特征空间时，可以从它们自己的角度保留药物和病毒之间的相似性。基于 CMNMF 模型，我们提出了一种名为 VDA-DLCMNMF 的深度学习方法，用于重新利用药物来对抗新病毒。VDA-DLCMNMF 首先使用其相应的潜在特征向量初始化药物和病毒的节点表示，以避免随机初始化，然后应用图卷积网络来优化它们的表示。对于任意一种药物，根据其表示计算其与新病毒相关联的概率。为了评估 VDA-DLCMNMF 的性能，我们针对 SARS-CoV-2 构建了三个 VDA 数据集，并在这三个数据集上进行了一系列实验。实验结果表明，VDA-DLCMNMF 具有有前途的预测准确性。此外，将 CMNMF 模型纳入深度学习为 SARS-CoV-2 的药物再利用提供了新的思路，因为分子对接实验的结果表明，VDA-DLCMNMF 鉴定的四种抗病毒药物具有治疗 SARS-CoV-2 感染的潜在能力。
