The Role of Artificial Intelligence in Drug Discovery

Drug discovery is traditionally a prolonged, costly, and high-risk enterprise, often exceeding ten years and billions of dollars for a single new molecular entity. Artificial Intelligence (AI) has emerged as a transformative technology that can dramatically shorten timelines, reduce costs, and enhance predictive accuracy throughout pharmaceutical research and development. AI algorithms particularly machine learning (ML), deep learning (DL), natural language processing (NLP), and reinforcement learning (RL) enable the mining and interpretation of massive biochemical, genomic, and clinical datasets. These capabilities support target identification, hit discovery, lead optimization, and clinical trial design. This review summarizes the evolution of AI in pharmaceutical sciences, outlines the principal algorithms and architectures currently used, and evaluates applications across the modern drug-discovery pipeline. Special attention is given to AI-based target discovery from multi-omics data, structure-based virtual screening, predictive ADME/Tox modeling, and data-driven drug repurposing. Representative industrial case studies, including AlphaFold, Atomwise, BenevolentAI, and Insilico Medicine, demonstrate the translation of AI concepts into tangible therapeutic candidates. The article also critically discusses persistent challenges data bias, model transparency, reproducibility, and regulatory acceptance and highlights emerging solutions such as explainable AI, federated learning, and quantum-enhanced modeling. AI is not a substitute for experimental science but an indispensable complement that can guide and accelerate hypothesis generation, compound design, and clinical validation. The integration of AI with computational chemistry and biological experimentation heralds a new era of precision-driven, cost-effective, and patient-centered drug discovery.