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The novel coronavirus disease, named COVID-19, emerged in China in December2019, and has rapidly spread around the world. It is clearly urgent to fightCOVID-19 at global scale. The development of methods for identifying drug usesbased on phenotypic data can improve the efficiency of drug development.However, there are still many difficulties in identifying drug applicationsbased on cell picture data. This work reported one state-of-the-art machinelearning method to identify drug uses based on the cell image features of 1024drugs generated in the LINCS program. Because the multi-dimensional features ofthe image are affected by non-experimental factors, the characteristics ofsimilar drugs vary greatly, and the current sample number is not enough to usedeep learning and other methods are used for learning optimization. As aconsequence, this study is based on the supervised ITML algorithm to convertthe characteristics of drugs. The results show that the characteristics of ITMLconversion are more conducive to the recognition of drug functions. Theanalysis of feature conversion shows that different features play importantroles in identifying different drug functions. For the current COVID-19,Chloroquine and Hydroxychloroquine achieve antiviral effects by inhibitingendocytosis, etc., and were classified to the same community. And Clomiphene inthe same community inibited the entry of Ebola Virus, indicated a similar MoAsthat could be reflected by cell image.

Accelerating drug repurposing for COVID-19 via modeling drug mechanism of action with large scale gene-expression profiles