Integrative strategies in drug discovery: Harnessing genomics, deep learning, and computer-aided drug design

The development of novel drugs increasingly relies on advanced omics technologies, including genomics, transcriptomics, proteomics, and metabolomics. These approaches provide insights into genetic mutations, biomarkers, and disease pathways. However, the analysis of large-scale genomic data poses significant challenges, necessitating sophisticated computational tools. Deep learning and computer-aided drug design (CADD) have emerged as powerful solutions, enabling the integration of genomic data to predict drug-target interactions with greater accuracy, reduce off-target effects, and identify optimal drug candidates earlier in the development process. By leveraging deep learning, researchers can rapidly analyze vast datasets, model complex biological pathways, identify novel drug targets, and design innovative therapeutics. High-throughput sequencing technologies, coupled with computer-aided tools, play a pivotal role in uncovering the intricate relationships between drugs, diseases, and genes. Additionally, genomic databases serve as invaluable resources for advancing drug discovery. This review explores the integration of high-throughput sequencing technologies, deep learning algorithms, and computer-aided drug design, highlighting their transformative impact on modern drug discovery and development.