Meng Yajie, Jin Min, Tang Xianfang, Xu Junlin
College of Computer Science and Electronic Engineering, Hunan University, Changsha, Hunan, 410082, China.
Appl Soft Comput. 2021 May;103:107135. doi: 10.1016/j.asoc.2021.107135. Epub 2021 Jan 23.
The novel coronavirus disease 2019 (COVID-19) pandemic has caused a massive health crisis worldwide and upended the global economy. However, vaccines and traditional drug discovery for COVID-19 cost too much in terms of time, manpower, and money. Drug repurposing becomes one of the promising treatment strategies amid the COVID-19 crisis. At present, there are no publicly existing databases for experimentally supported human drug-virus interactions, and most existing drug repurposing methods require the rich information, which is not always available, especially for a new virus. In this study, on the one hand, we put size-able efforts to collect drug-virus interaction entries from literature and build the Human Drug Virus Database (HDVD). On the other hand, we propose a new approach, called SCPMF (similarity constrained probabilistic matrix factorization), to identify new drug-virus interactions for drug repurposing. SCPMF is implemented on an adjacency matrix of a heterogeneous drug-virus network, which integrates the known drug-virus interactions, drug chemical structures, and virus genomic sequences. SCPMF projects the drug-virus interactions matrix into two latent feature matrices for the drugs and viruses, which reconstruct the drug-virus interactions matrix when multiplied together, and then introduces the weighted similarity interaction matrix as constraints for drugs and viruses. Benchmarking comparisons on two different datasets demonstrate that SCPMF has reliable prediction performance and outperforms several recent approaches. Moreover, SCPMF-predicted drug candidates of COVID-19 also confirm the accuracy and reliability of SCPMF.
2019年新型冠状病毒病（COVID-19）大流行在全球范围内引发了一场巨大的健康危机，并扰乱了全球经济。然而，针对COVID-19的疫苗和传统药物研发在时间、人力和资金方面成本过高。在COVID-19危机中，药物重新利用成为一种有前景的治疗策略。目前，尚无公开的实验支持的人类药物-病毒相互作用数据库，且大多数现有的药物重新利用方法需要丰富的信息，而这些信息并不总是可用，尤其是对于一种新病毒。在本研究中，一方面，我们投入了大量精力从文献中收集药物-病毒相互作用条目，并构建了人类药物病毒数据库（HDVD）。另一方面，我们提出了一种新方法，称为相似性约束概率矩阵分解（SCPMF），用于识别用于药物重新利用的新的药物-病毒相互作用。SCPMF在一个异构药物-病毒网络的邻接矩阵上实现，该网络整合了已知的药物-病毒相互作用、药物化学结构和病毒基因组序列。SCPMF将药物-病毒相互作用矩阵投影到药物和病毒的两个潜在特征矩阵中，这两个矩阵相乘时可重构药物-病毒相互作用矩阵，然后引入加权相似性相互作用矩阵作为药物和病毒的约束条件。在两个不同数据集上的基准比较表明，SCPMF具有可靠的预测性能，优于最近的几种方法。此外，SCPMF预测的COVID-19候选药物也证实了SCPMF的准确性和可靠性。
