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Chemical research is entering a new era with the introduction of Graph Neural Networks (GNNs), a powerful form of artificial intelligence designed to understand relationships within complex systems. In chemistry, molecules can naturally be represented as graphs—atoms act as nodes and chemical bonds form the connections between them. Graph Neural Networks leverage this structure to analyze molecular interactions, predict properties, and forecast chemical reactions with remarkable accuracy. Traditional computational chemistry methods often require extensive simulations and significant computing resources. GNNs dramatically accelerate this process by learning patterns from vast chemical datasets. Once trained, these models can quickly predict molecular stability, reactivity, toxicity, and other properties, making them invaluable for drug discovery, materials science, and environmental chemistry. One of the most exciting applications of GNNs is in reaction prediction. By analyzing thousa...