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@article{209294,
        author = {Hindavi S. Adhav and Shivani Sonar and Rupali Gite and Dipali Bhaskar and Pooja Dengale},
        title = {AI-ASSISTED QSAR AND MOLECULAR MODELING IN MODERN DRUG DISCOVERY},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {13},
        number = {5},
        pages = {1841-1853},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=209294},
        abstract = {Artificial intelligence (AI) is transforming modern drug discovery by improving the speed, accuracy, and efficiency of computational approaches used to identify and optimize promising drug candidates. Quantitative structure–activity relationship (QSAR) modeling, when integrated with machine learning and deep learning, enables prediction of biological activity from molecular descriptors, fingerprints, and structural features. Molecular modeling techniques, including molecular docking, molecular dynamics, and virtual screening, provide complementary information about ligand–target interactions, binding behavior, and molecular stability. AI-assisted integration of these approaches can support rapid compound prioritization, lead optimization, selectivity assessment, and early prediction of pharmacokinetic and toxicity-related properties. Advanced algorithms identify complex relationships within large and heterogeneous chemical datasets that may be difficult to recognize using conventional methods. However, challenges involving data quality, model interpretability, applicability domains, validation, and reproducibility remain important. This review discusses the principles, applications, advantages, limitations, and future potential of AI-assisted QSAR and molecular modeling in contemporary drug discovery.},
        keywords = {Artificial Intelligence; QSAR; Molecular Modeling; Machine Learning; Drug Discovery; Molecular Docking; Virtual Screening; Lead Optimization.},
        month = {October},
        }