Copyright © 2026 Authors retain the copyright of this article. This article is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

@article{201691,
        author = {SHYAM KUMAR},
        title = {FROM MOLECULES TO MEDICINE : THE REVOLUTIONARY IMPACT OF AI IN MODERN PHARMACEUTICAL SCIENCES},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {12},
        pages = {5480-5491},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=201691},
        abstract = {The pharmaceutical industry is currently witnessing a paradigm shift, transitioning from traditional, labour-intensive trial-and-error methodologies to an era of data-driven precision. This review article, titled "From Molecules to Medicine: The Evolutionary Impact of AI in Pharmaceutical Sciences," explores the transformative journey of drug development through the lens of Artificial Intelligence (AI). Historically, bringing a new molecular entity from the laboratory bench to the patient’s bedside has been a Herculean task, often spanning over a decade and incurring billions of dollars in costs with a staggering failure rate. However, the integration of advanced computational tools ranging from Machine Learning (ML) and Deep Learning (DL) to Generative AI is fundamentally rewriting this narrative. At the molecular level, AI is revolutionizing target identification and lead optimization by navigating vast chemical spaces that were previously incomprehensible to human researchers. Tools like AlphaFold have unlocked the mysteries of protein folding, while generative models are now designing "de novo" molecules with specific therapeutic profiles, drastically shortening the early discovery phase. Beyond the flask, the evolutionary impact of AI extends into the clinical realm, where it optimizes trial designs, enhances patient stratification through genomic insights, and predicts pharmacokinetic behaviours with unprecedented accuracy the review highlights the role of AI in the rise of personalized medicine, moving away from the "one-size-fits-all" approach to tailor treatments based on individual genetic blueprints. While the potential for innovation is limitless, the article also critically examines the ethical frontiers, data privacy concerns, and the "black-box" nature of neural networks that pose regulatory challenges. By synthesizing current breakthroughs and future trajectories, this paper argues that AI is not merely a supplementary tool but the primary architect of a new pharmaceutical evolution, promising a future where medicines are discovered faster, priced lower, and delivered with surgical precision. Here is the simplified, more "human-sounding" English version of the abstract. It maintains the professional tone required for a review article but uses clearer language that is easier to read and present. The pharmacy field is changing rapidly, and Artificial Intelligence (AI) is the main reason behind this shift. In the past, developing a new medicine took over a decade, but by 2026, AI has made this process much faster and more efficient. This review article explains how AI is being used to discover new drugs in less time and how "Digital Twins" computer models of the human body are making clinical trials safer. Beyond research, AI is also making a huge difference in hospitals and local pharmacies. It helps pharmacists choose the right dosage and prevents dangerous medication errors. While there are still challenges, such as keeping patient data private and training staff to use these new tools, the goal is clear. AI is not here to replace pharmacists; instead, it acts as a powerful assistant that allows them to focus more on patient care. This paper concludes that the future of pharmacy lies in the hands of "Digital Pharmacists" who use technology to make healthcare more personalized and accurate.},
        keywords = {AI in Pharmacy, Drug Discovery, Clinical Trials, Patient Safety, Digital Pharmacist, Pharmaceutical Innovation, Digital Twins, Machine Learning},
        month = {May},
        }