DRPADC: A novel drug repositioning algorithm predicting adaptive drugs for COVID-19

•A compressed sensing model based on a central kernel alignment and multicore learning algorithm, called DRPADC (drug repositioning algorithm predicting adaptive drugs for COVID-19), is proposed to help identify high confidence drug candidates for application in the discovery of potential COVID-19 therapeutic agents.•Adopt Weight K nearest known neighbors algorithm (WKNKN) to effectively reduce the sparsity of the drug-disease adjacency matrix.•Fusion of multiple similarity information of drugs and diseases using central kernel alignment multinuclear learning algorithms 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. [Display omitted]