Objective: The exponential growth of digital social platforms has not only connected individuals globally but has also provided a platform for users to freely express their experiences and viewpoints on topics spanning from consumer products and services to broader societal matters such as political issues. Within this expansive digital discourse, in the recent years, one notably discussed subject has been the SARS-CoV-2 (COVID-19) vaccines. In this article, our focus is on investigating the profound impact of neural networks in the analysis of sentiments expressed by people concerning the introduction and utilization of these vaccines. Background: Sentiment analysis, a critical facet of natural language processing (NLP), is replete with intricate associations in the linguistic landscape. Within its realms, many sophisticated methodologies, such as machine learning algorithms, including neural network architectures, are employed to decipher the intricate web of semantic relationships embedded in textual data, which include, but are not limited to, social media posts. From gathering business intelligence, to market research and competitor analysis, examining sentiments has found many practical uses. In the domain of COVID-19 vaccines, sentiment analysis has provided valuable insights into vaccine hesitancy, vaccine adoption rates, and public trust in the governmental setup and in the pharmaceutical industry. Methods: A systematic literature review (meta-analysis) was carried out to quarry scientific research on neural network-based analysis of sentiments about COVID-19 vaccines. Implementing a thorough search strategy, we isolated relevant articles and methodically examined them to discern key insights that contributed to our comprehension of the utility of neural networks in analyzing public opinion regarding COVID-19 vaccines. Conclusion: Our study provides insights affirming that neural networks have shown a surpassing capacity to discern intricate patterns within vast textual datasets. Their inherent ability to capture contextual nuances in language has enabled a nuanced understanding of diverse sentiments about COVID-19 vaccines. This has helped formulate strategies to alleviate negative sentiments about the vaccines leading to higher vaccine acceptance rates and management of the pandemic.

Objective: Techniques that are based on artificial intelligence, specifically machine learning, have played a major role in the enhancement of pharmacological methodologies and development of medical treatments, especially those that are individualized or those which fall in the province of precision medicine. In this article, we attempt to examine how graph neural networks have revolutionized certain important aspects of pharmacology.Background: Pharmacological data is replete with unidirectional as well as bidirectional associations, with regards to, for example, drug interactions, patient-centered medicine, precision medicine, multi-omics data analysis, drug discovery, and optimization of experimental processes, and other fields. These associations can be more readily modeled using advanced computational methods and machine learning techniques like graph neural networks. The revolutionary advancements in the field of data mining have further fueled the need to create models that can resolve pharmacological correlations and dependencies into facilely interpretable outcomes. Methods: We conducted a literature review to find those documents which provide relevant information about our objectives. With a comprehensive search plan in place, we sequestered applicable articles and studied them to identify pertinent points that assisted our understanding of graph neural networks as a tool to improvise, automate, and simplify the practical applications in pharmacology and pharmacotherapeutics.Conclusion: The review of relevant research has confirmed our hypothesis that graph neural networks can be used to create an innovative, lasting, and radical departure in pharmaceutical therapeutics. Graph Neural Networks can automate and simplify many tasks based on large and complex datasets which are inherent in pharmacological science. Such techniques can help us achieve innovative methods in therapeutics using extant pharmaceuticals and in the development of new drugs, and therefore bode well for the future of healthcare.