Artificial Intelligence in Pharmacy: Applications in Drug Discovery and Clinical Decision Support Systems

Abstract

Artificial intelligence (AI) transforms pharmacy by improving the speed, precision, and evidence base of pharmaceutical research and clinical practice. In drug discovery, AI assists in target identification, virtual screening, molecular docking, lead optimization, toxicity prediction, pharmacokinetic modeling, drug repurposing, and personalized drug development. In clinical decision support systems (CDSSs), AI supports diagnosis, therapy selection, drug-drug interaction alerts, medication error reduction, risk prediction, and patient-specific pharmaceutical care. The aim of this thesis is to describe the concept of AI in pharmacy and to examine its applications in drug discovery and clinical decision support systems. The thesis is based on a narrative review of scientific literature, regulatory documents, and healthcare technology reports. The study highlights that AI can reduce research time, improve decision-making, enhance patient safety, and support precision medicine; however, issues such as data quality, algorithmic bias, privacy, explainability, regulatory validation, and human oversight remain important. The conclusion of this thesis is that AI should not replace pharmacists or clinicians but should act as a decision-support tool that strengthens evidence-based pharmaceutical care. Future pharmacy practice will require pharmacists who understand digital health, data science, pharmacovigilance, ethics, and AI-assisted clinical reasoning.