Simple GNN Regularisation for 3D Molecular Property Prediction and Beyond
Graph Neural Networks (GNNs) have been proven effective across a wide range of molecular property prediction and structured learning problems. However, their efficiency is known to be hindered by practical challenges such as oversmoothing. We introduce “Noisy Nodes”, a very simple technique for improved training of GNNs, in which we corrupt the input graph with noise, and add a noise correcting node-level loss. Adding noise helps overfitting, and the noise correction loss helps ameliorate oversmoothing by encouraging diverse node latents. Our regulariser applies well-studied methods in simple, straightforward ways which allows even generic architectures not designed for quantum chemistry to achieve state of the art results. We also demonstrate the effectiveness of Noisy Nodes with non-spatial architectures on Open Graph Benchmark (OGB) datasets. Our results suggest Noisy Nodes can serve as a complementary building block in the GNN toolkit for 3D molecular property prediction and beyond.
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OGB
OC20
