## 5 Conclusions  
This paper presents the preliminary results of an exploratory research on the feasibility of deep learning to predict the efficacy of compounds for drug discovery, using COVID-19 as a case study. A transfer learning approach was adopted for this task. One novelty of the proposed approach is the way the transfer learning strategy is implemented. We first trained the DenseNet, a pre-trained deep neural network, with the siRNA dataset, larger than the SARS-CoV-2 dataset with similar characteristics. The resulting model is then used to extract features from mock cells and active viral cells provided in \begin{table}
\begin{tabular}{l l} \hline \hline
**Compound** & **Probability** \\ \hline Sertaconazole & 0.98 \\ PKC 412 & 0.97 \\ L-Adrenaline & 0.97 \\ Isoetharine & 0.96 \\ desoximentasome & 0.95 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: List of Compounds with High Probability Scores (Classified as Active Virus). \begin{table}
\begin{tabular}{l l} \hline \hline
**Compound** & **Probability** \\ \hline GS-441524 & 0.08 \\ Remdesivir (GS-5734) & 0.08 \\ CX-4945 & 0.13 \\ Aloxistatin & 0.18 \\ Calcipotriene & 0.21 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: List of compounds with low probability scores (classified as mock). Figure 7: Histograms of probability scores for different compounds. In each plot, for each compound, the horizontal axis is divided in to 7 bins. The vertical axis shows the number of samples in each bin. the SARS-CoV-2 dataset. Thus, we used transfer learning twice from the ImageNet to the siRNA image dataset and then from the siRNA dataset to the SARS-CoV-2 dataset. This cascade transfer learning approach produced superior results for the case study. Another novelty of the approach is using a SoftMax layer as the output layer for the classifier, which produces probability (equivalently efficacy) scores for classifying viral cells treated with different compounds, which allows users to analyze test results with a statistical method. Experimental results demonstrated that the model was able to identify highly promising compounds, which were consistent with those identified in the literature and the drugs that are now under clinical trials. It must be noted that though the proposed transfer learning strategy is more explainable compared with a classification approach that outputs a hard binary decision, owing to the fact that it produces efficacy scores for candidate compounds in treating viral cells, the model on the whole is not yet transparent. For example, we have not yet been able to explain the properties of the features linked to either viral cells or mock cells. A way to improve the model's explainability is to link the features extracted by the model to biomarkers of mock and viral cells with a functional relationship which may also be realized by a data-driven model. Although this paper is limited in its applicability due to the requirements of obtaining a dataset to train the deep learning model, this research encourages us. The deep learning model trained only on the infected and healthy cells was then able to rank candidate compounds, among which some promising ones were under clinical trials. In theory, transfer learning serves to accelerate a drug discovery process significantly. It produces a list of compounds relatively effective on viral cells, thus providing a baseline for speeding up preclinical trials. With cases growing exponentially in a pandemic, the longer treatments and preventative measures develop, the more the situation worsens. Deep learning is a viable tool to combat future COVID-19-like outbreaks. Conceptualization, A.K.I. and D.Z. ; methodology, A.K.I. and D.Z. ; software, A.K.I. and D.Z. ; validation, A.K.I. ; formal analysis, D.Z. and A.K.I. ; writing--original draft preparation, D.Z. ; writing--review and editing, D.Z. and A.K.I. ; visualization, A.K.I. and D.Z. ; supervision, A.K.I. ; project administration, A.K.I. All authors have read and agreed to the published version of the manuscript.