Computational drug repositioning using similarity constrained weight regularization matrix factorization: A case of COVID-19

Abstract

Amid the COVID-19 crisis, we put sizeable efforts to collect a high number of experimentally validated drug-virus association entries from literature by text mining and built a human drug-virus association database. To the best of our knowledge, it is the largest publicly available drug-virus database so far. Next, we develop a novel weight regularization matrix factorization approach, termed WRMF, for in silico drug repurposing by integrating three networks: the known drug-virus association network, the drug-drug chemical structure similarity network, and the virus-virus genomic sequencing similarity network. Specifically, WRMF adds a weight to each training sample for reducing the influence of negative samples (i.e. the drug-virus association is unassociated). A comparison on the curated drug-virus database shows that WRMF performs better than a few state-of-the-art methods. In addition, we selected the other two different public datasets (i.e. Cdataset and HMDD V2.0) to assess WRMF's performance. The case study also demonstrated the accuracy and reliability of WRMF to infer potential drugs for the novel virus. In summary, we offer a useful tool including a novel drug-virus association database and a powerful method WRMF to repurpose potential drugs for new viruses.

Keywords: association prediction; drug-target; drug-virus association; matrix factorization; similarity constrained.