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@article{209851,
        author = {Mrs.Harsimrat kaur},
        title = {Artificial Intelligence in Organic Chemistry: Transforming Molecular Design, Reaction Prediction, and Sustainable Synthesis (Review Article )},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {13},
        number = {5},
        pages = {3507-3510},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=209851},
        abstract = {Artificial intelligence (AI) is transforming organic chemistry by enabling data-driven molecular design, reaction-outcome prediction, retrosynthetic planning, reaction-condition optimization, and the development of more environmentally friendly synthetic pathways. Machine learning methods can detect trends in vast chemical datasets, but deep learning and generative approaches can propose compounds and transformations that would be impossible to discover using traditional trial-and-error procedures alone. These methods are also being connected with robotic platforms and automated experimentation to provide closed-loop optimization, in which forecasts lead experiments and experimental results update models. However, practical adoption is hampered by incomplete and biased datasets, inconsistent reaction representations, uncertainty in model predictions, limited transferability beyond training distributions, and the difficulty of translating computational performance into reproducible laboratory results. Sustainability claims must also be carefully assessed using specified indicators such as waste generation, solvent toxicity, energy consumption, atom economy, and process mass intensity. This narrative review outlines significant AI concepts and their applications in organic chemistry, evaluates present limits, and identifies research goals for AI-enabled synthesis that is reliable, interpretable, and environmentally responsible},
        keywords = {Artificial intelligence, organic chemistry, machine learning, reaction prediction, retrosynthesis, generative models, green chemistry, and automated synthesis.},
        month = {October},
        }