Graph neural networks (GNNs) have shown remarkable success in molecular property prediction as key models in geometric deep learning. Meanwhile, Kolmogorov–Arnold networks (KANs) have emerged as powerful alternatives to multi-layer perceptrons, offering improved expressivity, parameter efficiency and interpretability. To combine the strengths of both frameworks, we propose Kolmogorov–Arnold GNNs (KA-GNNs), which integrate KAN modules into the three fundamental components of GNNs: node embedding, message passing and readout. We further introduce Fourier-series-based univariate functions within KAN to enhance function approximation and provide theoretical analysis to support their expressiveness. Two architectural variants, KA-graph convolutional networks and KA-augmented graph attention networks, are developed and evaluated across seven molecular benchmarks. Experimental results show that KA-GNNs consistently outperform conventional GNNs in terms of both prediction accuracy and computational efficiency. Moreover, our models exhibit improved interpretability by highlighting chemically meaningful substructures. These findings demonstrate that KA-GNNs offer a powerful and generalizable framework for molecular data modelling, drug discovery and beyond. Li et al. developed KA-GNNs, graph neural network architectures enhanced by Kolmogorov–Arnold networks, which improve accuracy and interpretability in molecular property prediction and extend geometric deep learning to scientific domains.