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@article{205302,
        author = {Anjali Salunke and shrushti Ganbote},
        title = {Decoding the future: How ARTIFICIAL INTELLIGENCE (AI) is revolutionizing drug discovery},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {13},
        number = {1},
        pages = {6661-6665},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=205302},
        abstract = {The process of drug discovery is inherently complex, time-consuming, and expensive, with a significant rate of failures along the way. However, the integration of artificial intelligence (AI) has notably transformed this field, allowing for quicker and more efficient identification and development of potential therapies. AI methodologies, such as machine learning, deep learning, and natural language processing, are increasingly utilized throughout various phases of drug discovery, including target identification, hit generation, and lead optimization. These techniques enhance the analysis of extensive biological and chemical datasets, thereby improving decision-making and lowering experimental expenses. Furthermore, AI is crucial for drug repurposing and optimizing the design of clinical trials by enhancing patient stratification. Despite these advancements, challenges related to data quality, model interpretability, and regulatory approval continue to be significant concerns. This review sheds light on the latest developments in AI-driven drug discovery, examines current limitations, and presents future outlooks for the integration of AI with traditional pharmaceutical research to expedite the development of safe and effective medications.},
        keywords = {Artificial intelligence, Machine learning, Target identification, drug discovery, AI-limitations.},
        month = {June},
        }