###### Abstract
Machine learning has various applications in medicine, with one of the most significant benefits being drug discovery. Biological systems are incredibly complex and contain an immense amount of densely stored information, with the new forms of data allow for even more data for pharmaceutical companies to access and organize. The decision process of the neural network relies on its ability to identify specific characteristics of viral cells and healthy control cells, known as \"features,\" allowing it to analyze new data and classify the data as \"healthy\" or \"viral,\" while relative values of these features associated with each class allow the model to provide a probability score indicating its \"confidence\" in its classification [5; 6; 7; 12]. In addition, the broader availability of more powerful computers and deep learning algorithms helps increase the accessibility of deep neural networks (DNNs) [12; 13; 15; 16], and allows for an increasingly robust variety of applications for deep learning within the pharmaceutical industry. There are various effective methods of machine learning, which can be divided into supervised and unsupervised learning techniques. Supervised learning is valuable in its ability to predict labels of data samples, while unsupervised learning is valuable in its ability to categorize and cluster data in ways that the creator may not be aware of. Our model is based on supervised learning, specifically a type of artificial neural network known as a convolutional neural network (CNN). It is well known that different forms of neural networks are suitable in modeling and analyzing different types of data. For example, recurrent neural networks are more capable of analyzing time-variable neural networks, while deep CNNs [3] excel at image processing. Deep learning is useful in its ability to identify biomarkers and build predictive models on the possible effectiveness of a drug, mainly through its ability to identify which drugs lead to the most significant changes. Despite their apparent value in rapidly accelerating the drug discovery process, it must be noted that neural networks still face challenges in selecting suitable architectures and in data preparation. In order to effectively identify and test targets and compounds, data must be standardized, comprehensive, and of sufficient quantity. Even then, the model is subject to overfitting, where it begins to recognize the noise of the training data, resulting in reduced performance. Overfitting can be combated with techniques such as regularization, which places a penalty on more complex models, and may also be helped by further processing and cleaning of data [6]. Our focus in this paper is to identify or validate promising compounds and leads for combating viral cells, particularly SARS-CoV-2 viral cells. In the literature, researchers have presented a wide selection of methods for the analysis of Recursion's dataset, with most methods arriving at the same conclusion on the most effective drugs, some of which have also been under clinical trials. For the RxRx19a dataset, there have been several research studies that utilized said dataset. In Recursion's in-house analysis, they concluded that GS-441524 was the clear front runner in terms of preventing cell damage, with Remdesivir, Cx-4945, and Alimitrine all being relatively close to each other in terms of efficacy score [5]. Another study by researchers from the Simon Frasier University and the University of British Columbia found the leading candidates for viral suppression were Remdesivir, GS-441524, and Aloxistatin, as well as the drugs Mebendazole, Oxibendazole, and Albendazole [17]. This group of researchers proposed to use support vector machines to identify potential lead compounds, while our model relies on transfer learning. Our analysis, Recursion's analysis, and analysis from the researchers at Simon Frasier University arrive at similar compounds identified as the most promising, though our approach uses probability as an equivalent efficacy score for each candidate compound.