Interindividual variability in drug response is driven by several extrinsic and intrinsic factors promoted by changes in the activity or availability of drug-metabolizing enzymes (DMEs), receptors, channels, and other proteins involved in drug pharmacokinetics and pharmacodynamics, respectively. Genomics promotes a thorough understanding of various molecular mechanisms and pathways in the human system, supported by pharmacogenomics (PGx) and personalized medicine. However, with the technological advances of the genomic era and the ability to decipher the molecular makeup of cells at increasing precision and resolution, it has become clear that the “one gene, one protein, one function” paradigm does not fully explain the complex functional phenotypes of organisms. However, combining high-throughput techniques, especially next-generation sequence (NGS), with analytical tools will allow complex understanding of the interactions between drugs and non-drug substances for an effective treatment. NGS studies have integrated a systems biology approach and combined sequencing data with other types of information, for instance, protein family information, pathway, or protein-protein interaction (PPI) networks, in a computation integrative analysis. Additionally, advancements in artificial intelligence (AI) and machine learning (ML) methods have been greatly aided in analyzing, learning, and explaining pharmaceutical-related big data in the drug discovery process.