A key element of GNNs is the pooling function which combines atom feature vectors into molecular fingerprints. Most previous works use a standard pooling function to predict a variety of properties. However, unsuitable pooling functions can lead to unphysical GNNs that poorly generalize.
Jul 27, 2022 · A key element of GNNs is the pooling function which combines atom feature vectors into molecular fingerprints. Most previous works use a ...
A key element of GNNs is the pooling function which combines atom feature vectors into molecular fingerprints. Most previous works use a standard pooling ...
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Jul 29, 2022 · We rec- ommend using sum pooling for the prediction of properties that depend on molecular size and compare pooling functions for properties ...
We compare and select meaningful GNN pooling methods based on physical knowledge about the learned properties. The impact of physical pooling functions is ...
Transfer learning with graph neural networks for improved molecular property prediction in the multi-fidelity setting. · Chemprop: A Machine Learning Package for ...
Nov 14, 2022 · We compare and select meaningful GNN pooling methods based on physical knowledge about the learned properties. The impact of physical pooling ...
Jan 24, 2024 · Physical pooling functions in graph neural networks for molecular property prediction · Schweidtmann, A. M. · Rittig, J. G. · Weber, J. · Grohe, M.
Nov 10, 2023 · These networks offer a promising avenue by modeling interatomic reactions between constituent atoms through attention scores, which indicate ...
We compare and select meaningful GNN pooling methods based on physical knowledge about the learned properties. The impact of physical pooling functions is ...