AI is changing the way chemists do chemical research from performing experiments based on their intuition to predicting the outcome of experiments and using data-driven methods. This paper provides a broad overview of the use of AI in chemistry, including reaction prediction, retrosynthetic analysis, molecular properties, spectroscopic data analysis, and performance of research through automated workflows. The use of new technologies like graph neural networks and transformer architectures results in improved accuracy, scalability and computational efficiency. AI has been applied in many areas of chemical research as well as drug discovery, materials design, environmental chemistry and the optimisation of industrial processes. However, the ongoing issues of data reliability, model interpretability, and reproducibility continue to make widespread use of AI less common. There is an emphasis on the important role that domain expertise plays in confirming AI-generated results. Ultimately, this paper finds that although AI cannot replace the foundational reasoning of chemists, it can be used effectively as a complementary tool to revolutionise research methodologies and transform chemical education.
K et al. (Thu,) studied this question.
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