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Drug repurposing can accelerate the identification of effective compounds forclinical use against SARS-CoV-2, with the advantage of pre-existing clinicalsafety data and an established supply chain. RNA viruses such as SARS-CoV-2manipulate cellular pathways and induce reorganization of subcellularstructures to support their life cycle. These morphological changes can bequantified using bioimaging techniques. In this work, we developed DEEMD: acomputational pipeline using deep neural network models within a multipleinstance learning framework, to identify putative treatments effective againstSARS-CoV-2 based on morphological analysis of the publicly available RxRx19adataset. This dataset consists of fluorescence microscopy images of SARS-CoV-2non-infected cells and infected cells, with and without drug treatment. DEEMDfirst extracts discriminative morphological features to generate cellmorphological profiles from the non-infected and infected cells. Thesemorphological profiles are then used in a statistical model to estimate theapplied treatment efficacy on infected cells based on similarities tonon-infected cells. DEEMD is capable of localizing infected cells via weaksupervision without any expensive pixel-level annotations. DEEMD identifiesknown SARS-CoV-2 inhibitors, such as Remdesivir and Aloxistatin, supporting thevalidity of our approach. DEEMD can be explored for use on other emergingviruses and datasets to rapidly identify candidate antiviral treatments in thefuture}. Our implementation is available online athttps://www.github.com/Sadegh-Saberian/DEEMD

DEEMD: Drug Efficacy Estimation against SARS-CoV-2 based on cell Morphology with Deep multiple instance learning