Given that the usual process of developing a new vaccine or drug for COVID-19 demands significant time and funds, drug repositioning has emerged as a promising therapeutic strategy. We propose a method named DRPADC to predict novel drug-disease associations effectively from the original sparse drug-disease association adjacency matrix. Specifically, DRPADC processes the original association matrix with the WKNKN algorithm to reduce its sparsity. Furthermore, multiple types of similarity information are fused by a CKA-MKL algorithm. Finally, a compressed sensing algorithm is used to predict the potential drug-disease (virus) association scores. Experimental results show that DRPADC has superior performance than several competitive methods in terms of AUC values and case studies. DRPADC achieved the AUC value of 0.941, 0.955 and 0.876 in Fdataset, Cdataset and HDVD dataset, respectively. In addition, the conducted case studies of COVID-19 show that DRPADC can predict drug candidates accurately.

鉴于为 COVID-19 开发新疫苗或药物的通常过程需要大量时间和资金，药物重新定位已成为一种有前途的治疗策略。我们提出了一种名为 DRPADC 的方法，可以从原始的稀疏药物-疾病关联邻接矩阵中有效地预测新的药物-疾病关联。具体来说，DRPADC 使用 WKNKN 算法处理原始关联矩阵以降低其稀疏性。此外，CKA-MKL算法融合了多种相似度信息。最后，使用压缩感知算法来预测潜在的药物-疾病（病毒）关联分数。实验结果表明，DRPADC 在 AUC 值和案例研究方面具有优于几种竞争方法的性能。DRPADC在Fdataset中取得了0.941、0.955和0.876的AUC值，分别是 Cdataset 和 HDVD 数据集。此外，对 COVID-19 进行的案例研究表明，DRPADC 可以准确预测候选药物。