Targeting disease: Computational approaches for drug target identification

With the advancing technology, the way to drug discovery has evolved. The use of AI and computational methods have revolutionized the methods to develop novel therapeutics. In previous years, the methods to discover new drugs included high-throughput screening and bioassays which were labor-dependent, extremely expensive and had high probability to inaccurate results. The introduction of Computational studies has changed the process by introducing various methods to determine hit compounds and their methods of analysis. Methods such as molecular docking, virtual screening, and dynamics have changed the path to optimize and produce lead molecules. Similarly, network pharmacology also works on the identification of target proteins complex disease pathways with the help of protein-protein interactions and obtaining hub proteins. Various tools such as STRING database, cytoscape and metascape are employed in the study to construct a network between the proteins responsible for the disease progression and helps to obtain the vital target proteins, simplifying the process of drug-target identification. These approaches when employed together, results in obtaining results with better precision and accuracy which can be further validated experimentally, saving the resources and time. This chapter highlights the foundation of computational approaches in drug discovery and provides a detailed understanding of how these approaches are helping the researchers to produce novel solutions using artificial intelligence and machine learning.