Computational Approaches for Drug Target Identification

In the field of drug development, challenges often arise due to insufficient knowledge about drug targets, unpredictable pharmacokinetic responses upon target interaction, and off-target effects. These hurdles stem from the methods and population data used to identify targets, especially for complex diseases influenced by multiple genetic factors, creating a bottleneck in drug development. The initial stage involves identifying and validating drug targets, crucial for modulating disease states and achieving desired biological responses. Tools like Chemoinformatics play a vital role in in silico drug discovery by integrating data across various levels to enhance reliability. Techniques like chemical structure similarity searching, data mining, and machine learning facilitate drug research, predicting targets and summarizing interactions. Other tools like Omics analyzes genomics, metabolomics, and more, providing insights into complex diseases and early diagnostics using high-throughput technologies. Extracting information from scientific literature and databases supports research, utilizing resources like PubMed and Scopus. Machine learning methodology, i.e., network pharmacology, combines pharmacology, systems biology, and network science to understand drug interactions within biological systems. By integrating diverse biological datasets, it constructs detailed network models, improving our comprehension of drug–biological system interactions. In this chapter, these methods accelerate drug discovery, deepen understanding of biological systems, and revolutionize public health screening with their comprehensive and interdisciplinary approaches.