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@article{206958,
        author = {Viraj V. Naravane and Amol R. Chavan and Pavitra Kalal and Rohini P. Khot and Sandip B. Bankar and Arpita Patel},
        title = {AI-DRIVEN DRUG DISCOVERY: TRANSFORMING THE FUTURE OF PHARMACEUTICAL RESEARCH},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {13},
        number = {2},
        pages = {3712-3736},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=206958},
        abstract = {Artificial intelligence (AI) is reshaping pharmaceutical research by introducing data-driven, predictive, and generative approaches across the drug discovery and development continuum. Conventional drug discovery is constrained by lengthy timelines, substantial financial investment, high attrition rates, and the difficulty of translating preclinical findings into clinically effective therapies. AI offers a complementary strategy by integrating large and heterogeneous datasets, identifying complex biological relationships, predicting molecular properties, and prioritizing promising therapeutic candidates. Machine learning, deep learning, graph neural networks, natural language processing, reinforcement learning, generative models, and foundation models are increasingly applied to therapeutic target identification, virtual screening, de novo molecular design, quantitative structure–activity relationship modeling, drug–target interaction prediction, absorption–distribution–metabolism–excretion–toxicity assessment, drug repurposing, and clinical development. Advances in AI-enabled structural biology have further strengthened structure-guided drug discovery by improving the prediction of proteins and complex biomolecular interactions. Meanwhile, generative AI is expanding accessible chemical space by proposing novel molecules optimized against multiple pharmaceutical objectives. Recent clinical progress of AI-discovered and AI-designed candidates demonstrates the potential for computational discovery platforms to move beyond proof-of-concept applications. Nevertheless, substantial challenges remain, including limited data quality, dataset bias, insufficient model interpretability, uncertain generalizability, reproducibility concerns, biological complexity, regulatory requirements, and the continuing need for experimental validation. This review critically examines the technological foundations and major applications of AI across pharmaceutical research, with particular emphasis on target discovery, molecular design, predictive pharmacology, structural biology, preclinical development, and clinical translation. Emerging advances, regulatory considerations, current limitations, and future opportunities are also discussed. The convergence of AI, multimodal biomedical data, laboratory automation, and human scientific expertise may ultimately establish a more integrated, iterative, and evidence-driven paradigm for pharmaceutical innovation.},
        keywords = {Artificial intelligence; Drug discovery; Machine learning; Deep learning; Generative AI; Molecular design; Pharmaceutical research; Precision medicine},
        month = {July},
        }