## 4 Experimental Study  
The experiments were implemented using the MATLAB software on a computer server Nvidia DGX Workstation with 256 GB of RAM. First, we created a model to classify siRNA images based on the DenseNet161, and we then compared this model with different pre-trained models, such as VGG10, AlexNet, and GoogleNet. Table 1 shows the classification results of different models using siRNA image datasets. It is clear from the table that the DenseNet produced the best accuracy performance. We applied a 5-fold validation procedure to check the performance of the model, as shown in Figure 5. In this procedure, 80% of the data were randomly chosen for training and the remaining 20% for validation. This step was repeated five times until all mock/active viral cell images in the dataset were validated once. The results from these five tests were averaged. It must be stressed that in the training dataset, there were only active viral cells and mock cells. There were viral cells treated with different compounds in the test dataset, such as Remdesivir and GS-44152. We compare the effectiveness of our model with other well-known pre-trained models in classifying mock and active viral cells. Table 2 shows the results produced by these models in classifying mock/active viral cells. Sensitivity serves as a benchmark for the accuracy of a test's positive prediction, while specificity measures the accuracy of a test's null prediction [25]. In the context of our study, sensitivity is the ability of a particular model to determine active viral cells, while specificity is its ability to determine mock/control cells. The F1 score statistically measures the balance of sensitivity and specificity, indicating the ability of the model in preventing both false positives and false negatives [26]. Finally, the Kappa score is a way of statistically measuring the agreement throughout all the tested cases on a scale of less than and equal to 1, with less than 0 implying little agreement while 1 indicates perfect agreement [27]. Referencing Table 2, the cascade model outperformed other popular deep \begin{table}
\begin{tabular}{l c} \hline \hline
**Deep Learning Model** & **Accuracy** \\ \hline VGG16 & 81.2\% \\ GoogleNet & 83.1\% \\ VGG19 & 83.3\% \\ AlexNet & 77.4\% \\ DenseNet & 96.4\% \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Experimental results with different pre-trained models on siRNA dataset. Figure 5: Simplified model test procedure based on the USFDA MAQC II protocol [24]. neural network architectures. In comparison to the two popular deep learning models for the RxRx19 dataset, our cascade transfer learning model had the highest performance in terms of the given metrics. It must be noted that the vgg19 and GoogleNet models were pre-trained on the ImageNet dataset instead of the siRNA dataset before being retrained on the SARS-CoV-2 dataset. To further study the model's performance, its receiver operating characteristic (ROC) curve is plotted (refer to Figure 6). The area under the curve (AUC) is 0.98. We then tested the cascade transfer learning model by using it to rank the efficacy of the compounds used to treat COVID-19, as shown in the last block of Figure 5, with a score less than 0.5 indicating the promise of the drug as a lead. These test results are given in Tables 3 and 4 and Figure 7. In the figure, a low probability value, or equivalently a high efficacy score obtained by 1-probability, indicates that the corresponding compound effectively combats active viral cells. In each plot, the histograms of one high efficacy compound and one low efficacy compound are provided. The drugs identified to have the greatest impact on cell health are GS-441524, Remdesivir, CX-4945, Aloxistatin, and Calcipitene. Of these drugs, two are anti-viral drugs, specifically GS-441524 and Remdesivir, with GS-441524 being a primary component of Remdesivir, explaining their similarly low probability (or high efficacy) scores. In terms of the histograms shown in Figure 7, GS-441524 provides more consistent performance.