This work formulates antiviral repositioning as a matrix completion problem where the antiviral drugs are along the rows and the viruses along the columns. The input matrix is partially filled, with ones in positions where the antiviral has been known to be effective against a virus. The curated metadata for antivirals (chemical structure and pathways) and viruses (genomic structure and symptoms) is encoded into our matrix completion framework as graph Laplacian regularization. We then frame the resulting multiple graph regularized matrix completion problem as deep matrix factorization. This is solved by using a novel optimization method called HyPALM (Hybrid Proximal Alternating Linearized Minimization). Results on our curated RNA drug virus association (DVA) dataset shows that the proposed approach excels over state-of-the-art graph regularized matrix completion techniques. When applied to "in silico" prediction of antivirals for COVID-19, our approach returns antivirals that are either used for treating patients or are under for trials for the same.

中文翻译：

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DeepVir——“In Silico”抗病毒重新定位的图形深度矩阵分解：对 COVID-19 的应用

这项工作将抗病毒药物重新定位制定为矩阵完成问题，其中抗病毒药物沿行排列，病毒沿列排列。输入矩阵被部分填充，其中一些位于已知抗病毒药物对病毒有效的位置。抗病毒药物（化学结构和通路）和病毒（基因组结构和症状）的精选元数据被编码到我们的矩阵完成框架中，作为图拉普拉斯正则化。然后，我们将由此产生的多图正则化矩阵完成问题构建为深度矩阵分解。这是通过使用称为 HyPALM（混合近端交替线性化最小化）的新型优化方法来解决的。我们策划的 RNA 药物病毒关联 (DVA) 数据集的结果表明，所提出的方法优于最先进的图形正则化矩阵完成技术。当应用于 COVID-19 抗病毒药物的“计算机”预测时，我们的方法返回用于治疗患者或正在接受相同试验的抗病毒药物。