## 5 Discussion  
In order to increase efficiency and productivity in the pharmaceutical industry, it is necessary to process the vast amount of data generated by IoMT devices [22]. However, companies that rely on traditional computing methods may face challenges in monitoring, analyzing, and managing these data [1]. The COVID-19 pandemic has highlighted the limitations of conventional methods in drug and vaccine development, and new computing paradigms, such as AI, DL, and EC, can be utilized to address these challenges [5; 30]. Smartphones, smart gadgets, and other intelligent devices have become essential tools for data sharing and communication, particularly during emergencies [31]. Furthermore, the continuous advancements in mobile communication systems should be considered by medical-service providers. As more services become available online, it is predicted that the internet will play a significant role in satisfying the needs and expectations of both companies and customers. This section discusses the advantages and potential outcomes of using an EC approach for drug discovery. It highlights how increasing technological capacity has created the infrastructure to support EC-based strategies. * One of the benefits of this approach is the option to utilize DL-based methods and other AI-enabled computing techniques to strengthen healthcare systems. The demand for DL approaches in medical procedures is increasing, and more flexible technologies are needed to support different types of IoMT device. * Another benefit is the shifting of the main aspect of data processing, pattern recognition, and classification from the cloud to edge devices (EDs) located near users and vaccine candidates. This reduces latency and improves the speed of data processing, which is critical in emergency situations. Using EC-enabled IoMT devices can also increase the storage space for big data generated by IoMT devices, thus facilitating the handling of vast amounts of data. * Moreover, deploying this method can increase the level of security, especially regarding medical information. Any problems with safety can have fatal consequences for individuals and damage medical products. Therefore, boosting the security of the network is critical. * Lastly, reducing latency is the most significant benefit of using EC-enabled IoMT devices, as it can play a crucial role in saving lives, especially in emergencies. In summary, using an EC approach for drug discovery offers many advantages and capabilities that can improve the efficiency and productivity of the pharmaceutical industry. The assessment of the performance of high-tech platforms is crucial, and their applicability and implementation are among the key factors to consider. In this regard, the proposed platform is a promising solution, and there are some compelling pieces of evidence to support its potential. For example, a study investigating the potential of utilizing DL techniques for the IoT in an EC environment was conducted [8]. The authors demonstrated that DL can be a valuable tool with which to accurately extract information from raw sensor data in complex environments. To overcome the limitations of EC nodes' processing capabilities, the researchers developed a novel offloading strategy to enhance the performances of DL applications for the IoT. They evaluated the performance of this approach by executing multiple DL tasks in an EC environment and discovered that it outperformed other optimization solutions for DL in the IoT. The authors showed that it is possible and practical to apply deep learning to the IoT in an edge-computing environment, and that their new offloading strategy can help to improve the performances of IoT-based deep-learning applications in these environments [8]. Further evidence that the presented approach can be implemented is supplied through the research presented in an article highlighting the potential of DTL to mitigate pandemics [15].