A comprehensive narrative review of artificial intelligence applications in drug discovery and pharmaceutical practice

This comprehensive narrative review examines how artificial intelligence (AI) is being applied in drug discovery and pharmacy practice. In pharmaceutical research, methods such as machine learning (ML) and neural networks help predict key physicochemical properties, optimize pharmacokinetic and pharmacodynamic profiles, and support early toxicity assessment. These applications can accelerate the identification of promising drug candidates and make the design and conduct of clinical trials more efficient and cost-conscious. In clinical practice, AI supports medication management by automating prescription verification, assisting in dose selection, and detecting adverse drug reactions (ADRs) close to real time. AI based clinical decision support systems are increasingly used to reduce medication errors and to monitor patient adherence to treatment. High income countries are also deploying AI driven platforms in digital therapeutics, pharmaceutical supply chain management, and real time drug performance monitoring. Despite this progress, several barriers limit wider adoption. Biased or unrepresentative training data, the opaque “black box” nature of many models, and fragmented regulatory requirements remain major challenges. Ethical concerns related to patient privacy, data security, and algorithmic fairness require robust governance frameworks. Furthermore, the inherent complexity of biological systems and the shortage of professionals trained to interpret AI outputs constrain effective implementation. Overall, the potential of AI to reshape pharmaceutical science is substantial, but its long-term impact will depend on addressing regulatory and ethical issues and strengthening collaboration among researchers, healthcare professionals, and policy makers.