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@article{193156,
        author = {Ankita Banga and Sarabjit Kaur and Lochana Devi P and Mohammed Fayazuddin and Puja Karmakar and Sanjana Alai and Dipty D Bhattacharya},
        title = {Artificial Intelligence in Pharmaceutical Drug Discovery: From Target identification to Clinical Development},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {9},
        pages = {4291-4320},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=193156},
        abstract = {Pharmaceutical drug discovery has traditionally been a prolonged, costly, and high-risk process, characterized by linear workflows, heavy reliance on experimental trial-and-error, and high attrition rates during clinical development. Despite advances in molecular biology and medicinal chemistry, conventional approaches often fail to adequately capture the complexity of disease biology and patient heterogeneity, resulting in late-stage failures and limited translational success. In recent years, artificial intelligence (AI) has emerged as a transformative force in pharmaceutical research, fundamentally reshaping how drugs are discovered, optimized, and clinically evaluated. By integrating machine learning, deep learning, reinforcement learning, and natural language processing with large-scale biological, chemical, and clinical datasets, AI enables data-driven decision-making across the entire drug development pipeline.
This review presents a comprehensive and comparative analysis of conventional drug discovery paradigms and AI-integrated approaches, focusing on the complete continuum from target selection and validation to clinical trial design and execution. It examines how AI technologies have enhanced target identification through multi-omics analysis, improved hit discovery and lead optimization via predictive modeling and generative algorithms, and redefined clinical trials through patient stratification, adaptive study designs, and outcome prediction.  The paper further discusses the advantages and limitations of specific AI techniques employed at different stages, highlighting challenges related to data quality, model interpretability, ethical concerns, and regulatory compliance. Finally, it explores future perspectives for AI-driven pharmaceutical innovation, emphasizing the need for explainable, trustworthy, and ethically governed AI systems to ensure sustainable integration into drug development.
This work aims to provide a structured and in-depth understanding of how artificial intelligence has transitioned drug discovery from a largely empirical process to a predictive, integrated, and patient-centric scientific enterprise.},
        keywords = {Artificial intelligence; pharmaceutical drug discovery; target selection; machine learning; deep learning; clinical trials; virtual screening; lead optimization; regulatory compliance; ethics in AI; precision medicine},
        month = {February},
        }