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@article{195059,
        author = {NIRMALA MUSMADE and UMA P. MOL},
        title = {A REVIEW ON ARTIFICIAL INTELLEGENCE IN DRUG DISCOVERY AND DESIGNING},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {10},
        pages = {6650-6661},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=195059},
        abstract = {Artificial intelligence (AI) is quickly becoming an important part of modern drug discovery and design, helping scientists tackle many of the problems associated with traditional pharmaceutical research. Developing new medicines has always been a long, expensive, and risky process, often relying on repeated trial-and-error experiments. Today, advances in AI—particularly in machine learning, deep learning, natural language processing, reinforcement learning, and quantum computing—are changing this approach by making drug discovery faster, more accurate, and more efficient at every stage. This review explains how AI is being applied throughout the drug discovery pipeline, from identifying disease-related targets and predicting molecular properties to designing new drug candidates, studying protein structures, planning chemical synthesis routes, and improving clinical trial design. Special focus is given to deep learning techniques such as convolutional, recurrent, and graph neural networks, which have proven highly effective in predicting drug–target interactions, toxicity risks, and pharmacokinetic behavior. Breakthrough technologies like AlphaFold have further transformed structure-based drug design by allowing researchers to accurately predict the three-dimensional shapes of proteins directly from their amino acid sequences.
The article also explores newer applications of AI in areas such as clinical trial optimization, personalized medicine, self-driving laboratories powered by robotics, nanomedicine, and nanorobotics. These innovations enable automated experiments, more precise drug delivery, improved patient selection, and quicker, data-driven decisions, all of which help shorten development timelines. However, despite its strong potential, AI adoption in drug discovery still faces challenges, including issues with data quality, bias, limited transparency of models, high computational requirements, and regulatory approval.Overall, AI is not designed to replace human expertise but to work alongside scientists and clinicians, supporting better insight, creativity, and decision-making. With ongoing collaboration among researchers, healthcare professionals, regulators, and industry partners—and as ethical and regulatory frameworks continue to develop—AI is expected to play an increasingly important role in creating safer, more effective, and more personalized therapies, ultimately improving patient care and global health outcomes.},
        keywords = {Artificial Intelligence (AI), Drug Discovery, Drug Design, Machine Learning, Deep Learning, De Novo Drug Design.},
        month = {March},
        }