Artificial Intelligence in Pharmaceutical Sciences: Accelerating Drug Discovery from Molecules to Medicine

Artificial intelligence (AI) has become a transformative force in pharmaceutical sciences, reshaping the paradigms of drug discovery and clinical development. Conventional research approaches are often time-consuming, expensive, and inefficient, yielding success rates below 10%. The integration of advanced computational techniques such as machine learning (ML), deep learning (DL), and computational biology has markedly enhanced accuracy and productivity in key phases, including target identification, molecular docking, lead optimization, and drug repurposing.This review consolidates and evaluates emerging progress in AI-driven molecular modeling, predictive analytics, and explainable AI frameworks that collectively accelerate the drug discovery pipeline. It further discusses real-world applications from leading innovators like DeepMind, Insilico Medicine, and BenevolentAI, demonstrating how AI-powered solutions are revolutionizing pharmaceutical research. Additionally, the paper examines the growing role of AI in clinical trial management, enabling improved patient stratification, optimized study design, and adaptive methodologies, particularly evident in investigations concerning inflammatory bowel disease (IBD).Finally, the review addresses enduring limitations such as data inconsistency, model interpretability, and ethical challenges associated with AI adoption. It also envisions future directions, highlighting the potential integration of quantum AI, hybrid intelligence systems, and multi-omics approaches for advancing precision, transparency, and innovation across modern drug development