###### Abstract  
In this research, we applied deep learning to rank the effectiveness of candidate drug compounds in combating viral cells, in particular, SARS-Cov-2 viral cells. For this purpose, two different datasets from Recursion Pharmaceuticals, a siRNA image dataset (RxRx1), which were used to build and calibrate our model for feature extraction, and a SARS-CoV-2 dataset (RxRx19a) was used to train our model for ranking efficacy of candidate drug compounds. The SARS-CoV-2 dataset contained healthy, uninfected control or \"mock\" cells, as well as \"active viral\" cells (cells infected with COVID-19), which were the two cell types used to train our deep learning model. In addition, it contains viral cells treated with different drug compounds, which were the cells not used to train but test our model. We devised a new cascade transfer learning strategy to construct our model. We first trained a deep learning model, the DenseNet, with the siRNA set, a dataset with characteristics similar to the SARS-CoV-2 dataset, for feature extraction. We then added additional layers, including a SoftMax layer as an output layer, and retrained the model with active viral cells and mock cells from the SARS-CoV-2 dataset. In the test phase, the SoftMax layer outputs probability (equivalently, efficacy) scores which allows us to rank candidate compounds, and to study the performance of each candidate compound statistically. With this approach, we identified several compounds with high efficacy scores which are promising for the therapeutic treatment of COVID-19. The compounds showing the most promise were GS-441524 and then Remdesivir, which overlapped with these reported in the literature and with these drugs that are approved by FDA, or going through clinical trials and preclinical trials. This study shows the potential of deep learning in its ability to identify promising compounds to aid rapid responses to future pandemic outbreaks. COVID-19; drug discovery; deep learning; cascade transfer learning; DenseNet +
Footnote †: journal: Applied  
which is hugely time and labor-intensive, while also subject to human error. Due to the time constraints of such a process, the number of drugs that may be analyzed is limited. As such, for a situation such as COVID-19, which both spreads rapidly and has a risk of mutation, any method of streamlining drug discovery can serve to save countless lives. A viable approach in such a case is to use a deep learning model to identify promising compounds and to validate these compounds through clinical trials [6]. Drug discovery using machine learning methods such as support vector machines and neural networks has been proven effective in attempting to lower the prohibitive cost and speed up the process, as opposed to the extensive process of human experimentation [7; 8; 9; 10; 11]. In this research, we attempt to demonstrate the potential of deep learning [12; 13], a neural network-based machine learning technique, to determine promising drug compounds, using COVID-19 as a proof of concept in order to produce a response for future outbreaks [6; 11; 12]. Currently, the drug selection process involves target identification and validation, which aims to identify which molecules vary based on the viral status of the cells in order to identify possible gauges for the change caused by drugs [9; 14]. Target identification also aims to predict the druggability of a target molecule, or the extent to which the activity of the target compound may be changed through therapeutics. The drug then faces compound screening and lead discovery, which aims to identify promising compounds and drugs through determining which compounds result in the most significant variability of the resultant cells and identifying the most promising compounds as leads, which then proceed to the preclinical and clinical development trials. In this paper, we explore the ability of neural networks to accelerate the first two stages of target identification and validation, as well as compound screening and lead verification.