## 4 EC, DL, IoMT, and Drug Discovery  
This section discusses the potential of integrated approaches that utilize deep learning, CC, and EC to support drug discovery [11; 22]. The proposed framework surveys software components, such as CC, EC, and DL, as well as hardware components, such as edge devices (ED) and IoMT, to enhance the traditional process of designing and approving drugs and vaccines. In order to present this framework, it is important to focus on cutting-edge EC technology and consider recent advancements in the field. While this framework has shown promise in accelerating the process of designing COVID-19 vaccines, it can also be applied to discovering vaccines for other diseases, and it serves as a novel and intelligent approach in drug discovery [23; 24]. The framework for drug discovery is represented in Figure 1, which shows a schematic diagram of the seven layers involved in the process. The layers are the virus identification, the prediction of suitable virus antigens, the identified antigens, the immunization of animals, the analysis of the animals' responses, clinical trials, and approved vaccines. Although Layer 6 (clinical trials) is the most significant part of the framework, as it uses EC to monitor vaccine candidates and analyze potential vaccines, the entire platform demonstrates how emerging technologies can be integrated to achieve the best results. Each layer can be examined from different perspectives, such as its purpose, the techniques used, and its inputs and outputs. The framework was designed to leverage the latest technological achievements and state-of-the-art EC to improve the traditional process of designing and approving drugs and vaccines. The platform has already shown its effectiveness in accelerating the process of COVID-19-vaccine design and can also be used as an intelligent approach for discovering vaccines for other diseases. In drug discovery, Layer 3 refers to the experimental phase, in which the antigens identified in Layer 2 are analyzed in various ways. This stage is critical in developing new methods for treatment and diagnosis. One useful tool in this stage is repertoire analysis, which allows the performances of adaptive immune systems to be analyzed and addressed. Through repertoire analysis, researchers can better understand the connection between antigen receptors and their specifications, leading to novel high-throughput approaches for describing these structures. Overall, Layer 3 is an important phase in drug discovery that can contribute to the development of new and effective treatments and diagnostic methods. The specific roles of each of the seven layers in the proposed framework are presented below. The first layer, referred to as Layer 1, is primarily concerned with the identification of viruses. This is an important task, and the methodologies used in this layer have been extensively researched and deployed in numerous investigations and medical studies. Virus identification involves a complex process of examination and analysis, which requires the use of various experimental techniques and analytical tools. In particular, bioinformatics play a crucial role in enabling the identification of viruses. Through bioinformatics operations, the crystal structure of the virus can be modeled and analyzed, leading to the identification of important biological features and sequence chains. It is worth noting that two critical outcomes can be derived from bioinformatics operations in virus identification: the biological assembly and the sequence-chain perspective. These outcomes provide valuable insights into the nature and behavior of the virus, which can be used to inform further investigations and studies. Overall, Layer 1 serves as a critical starting point in the proposed framework, enabling the identification of viruses and laying the groundwork for subsequent layers to build upon.