A comprehensive review on the integration of artificial intelligence and machine learning in pharmacology: Innovations, applications, and future directions

Abstract:

Artificial intelligence (AI) and machine learning (ML) are revolutionizing the field of pharmacology by providing innovative computational tools that enhance drug discovery, development, and therapeutic decision-making. These technologies facilitate the analysis of large and complex biological datasets, enabling the identification of novel drug targets, prediction of drug efficacy and toxicity, optimization of dosage regimens, detection of adverse drug reactions, and strengthening of Pharmacovigilance systems. This review summarizes recent advances in the application of AI and ML in pharmacology based on literature published over the past five years. Relevant information was collected from peer-reviewed national and international journals, scientific databases including PubMed, Google Scholar, and ResearchGate, as well as patents and book chapters. The review highlights the role of AI and ML in deciphering complex biological mechanisms, improving high-throughput drug screening, supporting personalized medicine, identifying meaningful data-driven patterns, and facilitating drug repurposing. The findings demonstrate that AI- and ML-based approaches significantly improve the efficiency, accuracy, and cost-effectiveness of pharmacological research by accelerating the identification of promising drug candidates, reducing development timelines, and enabling safer and more precise therapeutic interventions. These technologies also contribute to enhanced clinical decision-making and individualized treatment strategies through predictive analytics and risk assessment. Despite these advances, several challenges remain, including concerns regarding data quality, algorithm transparency, interpretability, regulatory compliance, and ethical management of patient information. Addressing these limitations through continued research, standardized frameworks, and responsible implementation will be essential to ensure the safe, reliable, and widespread integration of AI and ML into pharmacological research and clinical practice, ultimately advancing precision medicine and improving patient outcomes.