Role of Artificial Intelligence in Veterinary Pharmacology: From Drug Development to Personalized Therapy

Artificial intelligence (AI) is rapidly emerging as a transformative force in biomedical sciences, with growing relevance to veterinary pharmacology and One Health oriented research. Advances in machine learning, deep learning, natural language processing, and network-based modelling have enabled efficient analysis of complex, multidimensional biological and pharmacological datasets that exceed the capacity of traditional analytical approaches. In veterinary pharmacology, AI offers significant potential to accelerate drug discovery and development, improve species-specific pharmacokinetic–pharmacodynamic (PK–PD) modelling, enable precision dosing strategies, predict adverse drug reactions, and strengthen antimicrobial stewardship efforts. AI driven models facilitate early prediction of drug efficacy, toxicity, and pharmacokinetic behaviour, thereby reducing development costs and experimental burden while enhancing therapeutic safety and effectiveness across diverse animal species. Furthermore, integration of AI within a One Health framework supports cross-sectoral surveillance, particularly for antimicrobial resistance, by linking human, animal, and environmental health data. Despite these advances, challenges related to data quality, model interpretability, regulatory acceptance, ethical governance, and infrastructure limitations remain significant barriers to widespread adoption. This review synthesizes current concepts, tools, and applications of AI in veterinary pharmacology, highlights emerging opportunities in precision therapeutics and regulatory science, and discusses key challenges and future perspectives. Strategic interdisciplinary collaboration, standardized data frameworks, and explainable AI approaches will be essential to realizing the full potential of AI in advancing veterinary pharmacology and One Health objectives.