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@article{188926,
        author = {Govind Kumar and Dr. Alpana and Dr. Shivani Sharma},
        title = {AI in Drug Discovery: A Survey},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {7},
        pages = {3736-3740},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=188926},
        abstract = {Artificial Intelligence (AI) has grown to be innovative technology in the pharmaceutical industry, a theory shift in traditional drug invention. The conventional drug discovery development is a long, costly, and inefficient attempt that has a lofty rate of failure and hires over a decade and price over a billion money to develop another drug. The use of AI requires a modeling approach that faster evaluates a range of facts related to biological, compound, and pharmacological properties, pointedly speeding up innovation of a new possible drug. With a grouping of methodologies like deep systems, graph networks, in addition generative models, AI provides operational prediction, de novo design, with ADMET simulation with extraordinary accuracy. The blend of models of AI through biology, automated drug growth, and access to rich data has set a latest platform for revolution in pharmaceutical discovery. This section examines how AI tools have developed in drug discovery, involving analysis of pioneering searches, AI methodologies used, near with left challenges in this domain. The enormous potential on behalf of AI in revolutionizing global healthcare structures for capable, effective, and cheap medicine development strategies will similarly be emphasized.},
        keywords = {Artificial intelligence, drug discovery, machine learning, deep learning, generative models, graph neural networks, protein structure prediction},
        month = {December},
        }