Repositioning Drugs on Human Influenza A Viruses Based on a Novel Nuclear Norm Minimization Method

Abstract

Influenza A viruses, especially H3N2 and H1N1 subtypes, are viruses that often spread among humans and cause influenza pandemic. There have been several big influenza pandemics that have caused millions of human deaths in history, and the threat of influenza viruses to public health is still serious nowadays due to the frequent antigenic drift and antigenic shift events. However, only few effective anti-flu drugs have been developed to date. The high development cost, long research and development time, and drug side effects are the major bottlenecks, which could be relieved by drug repositioning. In this study, we proposed a novel antiviral Drug Repositioning method based on minimizing Matrix Nuclear Norm (DRMNN). Specifically, a virus-drug correlation database consisting of 34 viruses and 205 antiviral drugs was first curated from public databases and published literature. Together with drug similarity on chemical structure and virus sequence similarity, we formulated the drug repositioning problem as a low-rank matrix completion problem, which was solved by minimizing the nuclear norm of a matrix with a few regularization terms. DRMNN was compared with three recent association prediction algorithms. The AUC of DRMNN in the global fivefold cross-validation (fivefold CV) is 0.8661, and the AUC in the local leave-one-out cross-validation (LOOCV) is 0.6929. Experiments have shown that DRMNN is better than other algorithms in predicting which drugs are effective against influenza A virus. With H3N2 as an example, 10 drugs most likely to be effective against H3N2 viruses were listed, among which six drugs were reported, in other literature, to have some effect on the viruses. The protein docking experiments between the chemical structure of the prioritized drugs and viral hemagglutinin protein also provided evidence for the potential of the predicted drugs for the treatment of influenza.

Computational drug repurposing against SARS-CoV-2 reveals plasma membrane cholesterol depletion as key factor of antiviral drug activity

“…In summary, our two different prediction approaches showed reasonable performance (comparable to studies based on network medicine and chemical similarity [ 40 , 41 ]), to predict drugs with in vitro anti-SARS-CoV-2 activity, and also highlighted the importance of previously discussed SREBF1/2 transcription factors. Drug—SARS-CoV-2 signature similarities, and predicted probabilities of anti-SARS-CoV-2 activity is available in S1 Table .…”

MNNMDA: Predicting human microbe-disease association via a method to minimize matrix nuclear norm

“…Yang et al proposed to use a bounded nuclear norm regularization (BNNR) method to complete the drug-disease matrix under the low-rank assumption [39] . Liang et al proposed a novel antiviral drug repositioning DRMNN method, which formulated the drug repositioning problem as a low-rank matrix completion problem solved by minimizing the nuclear norm of a matrix with a few regularization terms [40] .…”