AI-Driven Genomic Mining for Drug Target Discovery and Disease Modelling

Abstract

Artificial intelligence (AI) is revolutionizing drug discovery and pharmacogenomics by allowing for the quick analysis of complex genomic data to reveal disease mechanisms and therapeutic targets. Sophisticated machine learning and deep learning algorithms speed up drug development by forecasting drug-target interactions, lead compound optimization, and modeling complex biological pathways with high accuracy. Through the integration of multi-omics data, such as genomic, transcriptomic, and proteomic information, AI systems are able to detect biomarkers and model intricate biological systems, enabling the path for precision medicine to be adapted to personal genetic blueprint. Such technologies have a profound effect in rare disease research, where AI makes the detection of genetic mutations associated with clinical phenotypes easier to enable the design of targeted therapies. In spite of these advances, challenges such as data standardization across diverse genomic datasets, algorithmic bias from underrepresentation in training data, and model interpretability pose important hurdles to clinical adoption. Ethical issues around genetic privacy also underscores the importance of strong frameworks to protect sensitive health data in collaborative analysis. Solving these challenges involves the use of explainable AI architectures to improve model transparency and reliability and multimodal data sources such as CRISPR-edited cellular models and single-cell sequencing results to improve predictive accuracy. Future research directions focus on enhancing computational infrastructure, designing adaptive regulatory policy, and putting equity at the forefront in genomic database curation to make AI-mediated healthcare benefits accessible throughout the world. By overcoming these technical and ethical barriers, AI can empower a revolution in precision medicine that accelerates drug discovery, personalizes patient treatment regimens, and enhances healthcare outcomes in a variety of diverse populations while promoting fairness and inclusivity in its applications. This revolutionary approach can potentially reshape global healthcare systems and drive therapeutic innovation at an unprecedented scale.