  Scaling deep learning models has been at the heart of recent revolutions in
language modelling and image generation. Practitioners have observed a strong
relationship between model size, dataset size, and performance. However,
structure-based architectures such as Graph Neural Networks (GNNs) are yet to
show the benefits of scale mainly due to the lower efficiency of sparse
operations, large data requirements, and lack of clarity about the
effectiveness of various architectures. We address this drawback of GNNs by
studying their scaling behavior. Specifically, we analyze message-passing
networks, graph Transformers, and hybrid architectures on the largest public
collection of 2D molecular graphs. For the first time, we observe that GNNs
benefit tremendously from the increasing scale of depth, width, number of
molecules, number of labels, and the diversity in the pretraining datasets. We
further demonstrate strong finetuning scaling behavior on 38 highly competitive
downstream tasks, outclassing previous large models. This gives rise to MolGPS,
a new graph foundation model that allows to navigate the chemical space,
outperforming the previous state-of-the-arts on 26 out the 38 downstream tasks.
We hope that our work paves the way for an era where foundational GNNs drive
pharmaceutical drug discovery.
