Applying Graph Neural Networks in Pharmacology

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.