We are all aware of the impact of the COVID-19 pandemic generated by the SARS-CoV-2 virus on the pharmaceutical sector. It appears to be a thing of the past, with essentially no new information about SARS-CoV-2 available in the public domain. However, issues continue to occur as a result of novel viral varieties and limits in existing immunization techniques. Despite tremendous progress in vaccine research, questions about their efficacy against novel viral varieties and the vulnerability of specific populations, such as immunocompromised people, remain. As a result, the emphasis is on the development of antiviral medications that target the SARS-CoV-2 main protease (Mpro), a critical enzyme in the virus's reproduction cycle. Mpro is a protein that is involved in the processing of viral polyproteins and is thought to be a prospective therapeutic target.

The protein kinase family is considered to be a promising target for Drug Discovery, as it plays a key role in controlling signal transduction in the cell. Abnormally elevated expression levels and mutations of protein kinase genes lead to disruptions in the cell signaling network, which is associated with pathological conditions such as inflammation and cancer.

Before we can dive into statistical learning, we must first create a solid foundation. The article "Best practices in Machine Learning in Chemistry" presents a collection of best practice standards and a matching checklist that can be used as a compass for navigating the challenging field of chemical machine learning. The researchers' goal was to maximize the validity and reproducibility of the conclusions and models derived from these best practices.

The amount of work done by humans was greatly decreased by the Industrial Revolution and the related mechanization. To lessen workloads in a manner like to that of the Industrial Revolution, numerous researchers are attempting to incorporate Artificial Intelligence (AI) and Machine Learning (ML) into various intellectual job processes.