## 4 EC, DL, IoMT, and Drug Discovery
Through a combination of experimental and analytical techniques, and with the aid of bioinformatics operations, Layer 1 provides a robust and reliable means of identifying viruses and advancing our understanding of their biology. A large number of crystal structures of molecule fragments that bind to the principal protease of the virus that causes COVID-19 were generated by a large-scale screening method. Therefore, independently of all the other valuable steps in the discovery of drugs for COVID-19, it is worth researching these structures. One of the significant issues in this layer is understanding the role of ribonucletic acid (RNA), a major polymeric molecule, from various biological perspectives, such as the presentation of genes, regulation, coding, and decoding. In summary, this layer is used for virus identification, and many achievements have been obtained in this area by using bioinformatics. Even if we ignore the use of AI methods to improve the performance of this layer, the bioinformatics-based method can be enhanced using DL or EC. Layer 2 of the proposed framework builds upon the virus identification in Layer 1, focusing specifically on the development of drug compounds that can be used to treat viral infections. Numerous potential drug compounds can be developed, with thousands of methods available for de novo drug development. To effectively navigate this vast array of potential drug compounds, an autonomous architecture is required to facilitate the discovery of suitable compounds. In this layer, DL techniques are used in combination with CC to predict suitable antigens, as well as B-cell and T-cell epitopes. This approach allows the rapid and accurate identification of potential drug candidates, streamlining the drug-discovery process and reducing the time and resources required. Characterizing T-cell immunity is a crucial aspect of this layer, as it plays a significant role in the development of vaccines and the treatment of virus-related infections. By identifying suitable T-cell epitopes, it becomes possible to design drugs that specifically target the virus and stimulate the immune system to fight off the infection. This approach is a powerful tool in the fight against viral infections, offering new possibilities for the development of effective treatments and vaccines [25]. The study of RNA-binding proteins is crucial for identifying the underlying causes of many disorders and diseases, as well as developing effective models for regulating biological systems. To achieve this, it is essential to develop a deep understanding of the sequence characteristics of RNA-binding proteins. However, this is a complex and challenging task, as there is a vast amount of data to process, and traditional methods may not be sufficient to handle the volume and complexity of the information involved. Fortunately, DL-based methods offer a promising solution to this problem, as they are capable of processing large amounts of data quickly and accurately. By utilizing DL algorithms in the CC framework, it is possible to develop highly effective predictors for RNA-binding protein sequences. This combination of methods is a practical and useful area of research, with the potential to revolutionize our understanding of these critical biological components. With the ability to process vast amounts of data and identify important sequence characteristics, DL-based methods hold great promise for advancing our understanding of RNA-binding proteins and their role in biological systems [14]. Another aspect of this stage involves profiling small molecules through image-based analysis. A critical aspect of understanding disease pathogenesis and cell physiology is identifying the pathways and intracellular molecules involved in cellular communication. The ability to decipher these complex interactions is crucial for developing effective treatments and advancing our understanding of cellular processes [26]. This stage has the potential to significantly expedite the prediction of the critical parameters necessary for vaccine design.