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Abstract
In pharmaceutical research, artificial intelligence (AI) has emerged as a transformative tool that tackles persistent issues like high development costs, protracted timetables, and low clinical success rates. AI speeds up several phases of drug development, including target identification, virtual screening, hit-to-lead optimisation, preclinical evaluation, and clinical trial design, through the integration of machine learning (ML), deep learning (DL), and advanced computational models. Prediction accuracy for protein structures, drug-target interactions, toxicity profiles, and ADMET attributes is improved by contemporary AI technologies like AlphaFold, DeepChem, Atomwise, and generative models like GANs and RNNs. De novo molecular design and AI-driven virtual screening make it possible to quickly find new candidates with enhanced drug-like characteristics. Additionally, adaptive clinical trial systems, phenotype-based screening, and AI-based digital twins greatly increase clinical success. AI continues to influence the future of pharmaceutical manufacturing, nanomedicine, and personalised medicine despite issues with data quality, transparency, and interaction with conventional workflows. When taken as a whole, AI provides a scalable, economical, and effective framework that is revolutionising innovation in contemporary drug research and discovery. AI continues to transform pharmaceutical research and personalised treatment in spite of obstacles like data quality, openness, and regulatory concerns.