  Drug repurposing can accelerate the identification of effective compounds for
clinical use against SARS-CoV-2, with the advantage of pre-existing clinical
safety data and an established supply chain. RNA viruses such as SARS-CoV-2
manipulate cellular pathways and induce reorganization of subcellular
structures to support their life cycle. These morphological changes can be
quantified using bioimaging techniques. In this work, we developed DEEMD: a
computational pipeline using deep neural network models within a multiple
instance learning framework, to identify putative treatments effective against
SARS-CoV-2 based on morphological analysis of the publicly available RxRx19a
dataset. This dataset consists of fluorescence microscopy images of SARS-CoV-2
non-infected cells and infected cells, with and without drug treatment. DEEMD
first extracts discriminative morphological features to generate cell
morphological profiles from the non-infected and infected cells. These
morphological profiles are then used in a statistical model to estimate the
applied treatment efficacy on infected cells based on similarities to
non-infected cells. DEEMD is capable of localizing infected cells via weak
supervision without any expensive pixel-level annotations. DEEMD identifies
known SARS-CoV-2 inhibitors, such as Remdesivir and Aloxistatin, supporting the
validity of our approach. DEEMD can be explored for use on other emerging
viruses and datasets to rapidly identify candidate antiviral treatments in the
future}. Our implementation is available online at
https://www.github.com/Sadegh-Saberian/DEEMD
