A Comprehensive Review on Deep Learning for Genomics and AI in Drug Discovery

The convergence of deep learning with genomics and artificial intelligence in drug discovery represents a transformative paradigm in biomedical research, offering unprecedented opportunities to accelerate scientific discovery and revolutionize healthcare. This comprehensive review examines the current applications, advancements, and future potential of deep learning technologies across genomic data analysis and pharmaceutical development pipelines. In genomics, deep learning has demonstrated remarkable capabilities in variant calling and annotation, with tools like DeepVariant achieving superior accuracy in identifying genetic variations, while transformer-based models like Enformer have revolutionized gene expression prediction from DNA sequences. The technology has also advanced epigenomic analysis, single-cell genomics, and functional genomics, enabling researchers to decode complex biological relationships previously inaccessible through traditional methods. In drug discovery, artificial intelligence is transforming every stage of the development pipeline, from target identification and validation to lead optimization and clinical trial design. Deep learning models excel in virtual screening of vast chemical libraries, de novo drug design through generative networks, and prediction of ADMET properties, significantly reducing the time, cost, and risk associated with bringing new therapeutics to market. The synergistic integration of genomic insights with AI-driven drug discovery enables precision medicine approaches, where treatments are tailored to individual genetic profiles. Multi-omics data integration through deep learning provides comprehensive disease understanding and facilitates the development of personalized therapeutic strategies. Despite remarkable progress, several challenges persist, including data availability and quality issues, model interpretability concerns, generalizability limitations, and ethical considerations surrounding privacy and algorithmic bias. Future directions include advanced multi-modal integration, reinforcement learning for drug design, digital twin technologies for personalized medicine, and the potential integration of quantum computing with AI. This review highlights how the intelligent convergence of deep learning in genomics and drug discovery is poised to unlock unprecedented capabilities in understanding life, combating disease, and developing next-generation therapeutics, ultimately promising a more personalized and effective approach to human healthcare.