Tang, Xianfang published the artcileIndicator regularized non-negative matrix factorization method-based drug repurposing for COVID-19, Synthetic Route of 55981-09-4, the main research area is human COVID19 nonneg matrix factorization method drug repurposing; COVID-19; biological networks; drug repurposing; non-negative matrix factorization; semi-supervised learning.
A novel coronavirus, named COVID-19, has become one of the most prevalent and severe infectious diseases in human history. Currently, there are only very few vaccines and therapeutic drugs against COVID-19, and their effi cacies are yet to be tested. Drug repurposing aims to explore new applications of approved drugs, which can signifi cantly reduce time and cost compared with de novo drug discovery. In this study, we built a virus-drug dataset, which included 34 viruses, 210 drugs, and 437 confirmed related virus-drug pairs from existing literature. Besides, we developed an Indicator Regularized non-neg. Matrix Factorization (IRNMF) method, which introduced the indicator matrix and Karush-Kuhn-Tucker condition into the non-neg. matrix factorization algorithm. According to the 5-fold cross-validation on the virus-drug dataset, the performance of IRNMF was better than other methods, and its Area Under receiver operating characteristic Curve (AUC) value was 0.8127. Addnl., we analyzed the case on COVID-19 infection, and our results suggested that the IRNMF algorithm could prioritize unknown virus-drug associations
Frontiers in Immunology published new progress about Algorithm. 55981-09-4 belongs to class esters-buliding-blocks, name is 2-((5-Nitrothiazol-2-yl)carbamoyl)phenyl acetate, and the molecular formula is C12H9N3O5S, Synthetic Route of 55981-09-4.
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