This paper introduces Message Passing Neural Networks (MPNNs), a unifying framework for graph-based deep learning, and applies it to quantum-chemistry property prediction, achieving state-of-the-art accuracy on the QM9 benchmark and approaching chemical accuracy on most targets. Its impact includes popularising graph neural networks, influencing subsequent work in cheminformatics, materials discovery, and the broader machine-learning community by demonstrating how learned message passing can replace hand-engineered molecular descriptors.
