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@article{192802,
        author = {Parth Zalavadiya and Shabir Ahmad Lone and Anushree Sunil Khedekar and Rushil Mohan and Suraj Kumar and Hiba Irshad and Bhuwaneshwar BU},
        title = {Artificial Intelligence in Pharmacology: Current Applications in Drug Discovery, Safety Monitoring and Personalized Therapy},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {9},
        pages = {2402-2422},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=192802},
        abstract = {Artificial intelligence (AI) has emerged as a transformative force in modern pharmacology, fundamentally reshaping the processes of drug discovery, safety evaluation, and therapeutic decision- making. The increasing complexity of pharmacological systems, coupled with the exponential growth of biomedical data generated from high-throughput experimental platforms, real-world clinical settings, and patient-centered digital technologies, has exceeded the analytical capacity of conventional statistical and rule-based methodologies. In this context, artificial intelligence provides a powerful computational framework capable of integrating heterogeneous data modalities, uncovering latent biological patterns, and generating predictive models that support evidence-based pharmacological innovation. This review critically examines the current applications of artificial intelligence in pharmacology, with a particular emphasis on its role in drug discovery, pharmacovigilance, and personalized therapy. By synthesizing advances in machine learning, deep learning, and natural language processing, this article highlights how AI-driven approaches are redefining target identification, lead optimization, adverse drug reaction detection, and individualized treatment strategies.
Beyond methodological advances, this review explores the translational and regulatory implications of artificial intelligence in pharmacology, addressing both its opportunities and inherent challenges. AI- enabled systems have demonstrated the potential to accelerate drug development timelines, improve safety signal detection, and enhance therapeutic precision; however, issues related to data quality, model interpretability, algorithmic bias, and ethical governance remain significant barriers to widespread adoption. Particular attention is given to the integration of real-world evidence and multi-omics data in AI-driven pharmacological modeling, as well as the evolving role of regulatory agencies in evaluating and validating AI-generated evidence. By providing a comprehensive and structured overview of current applications, methodological frameworks, and future directions, this review aims to offer a critical perspective on the role of artificial intelligence as a foundational pillar of next-generation pharmacology and precision medicine.},
        keywords = {Artificial intelligence; Pharmacology; Drug discovery; Pharmacovigilance; Drug safety monitoring; Personalized therapy; Precision medicine; Machine learning; Deep learning; Real-world evidence},
        month = {February},
        }