Papers › Directional Message Passing for Molecular Graphs

Directional Message Passing for Molecular Graphs

6 Mar 2020ICLR 2020 1arXiv:2003.03123archive 2025-07-28

Johannes Gasteiger, Janek Groß, Stephan Günnemann

Graph neural networks have recently achieved great successes in predicting quantum mechanical properties of molecules. These models represent a molecule as a graph using only the distance between atoms (nodes). They do not, however, consider the spatial direction from one atom to another, despite directional information playing a central role in empirical potentials for molecules, e.g. in angular potentials. To alleviate this limitation we propose directional message passing, in which we embed the messages passed between atoms instead of the atoms themselves. Each message is associated with a direction in coordinate space. These directional message embeddings are rotationally equivariant since the associated directions rotate with the molecule. We propose a message passing scheme analogous to belief propagation, which uses the directional information by transforming messages based on the angle between them. Additionally, we use spherical Bessel functions and spherical harmonics to construct theoretically well-founded, orthogonal representations that achieve better performance than the currently prevalent Gaussian radial basis representations while using fewer than 1/4 of the parameters. We leverage these innovations to construct the directional message passing neural network (DimeNet). DimeNet outperforms previous GNNs on average by 76% on MD17 and by 31% on QM9. Our implementation is available online.

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akirasosa/pytorch-dimenet mentioned on GitHubpytorchMIT report
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OutputBlock klicperajo/dimenet/dimenet/model/dimenet.py official repository ran licence not identified · pointer only · 09f7b0a12a0269c6 · report
DimeNet klicperajo/dimenet/dimenet/model/dimenet.py official repository unverified licence not identified · pointer only · 0afbb5155226e133 · report
InteractionBlock klicperajo/dimenet/dimenet/model/dimenet.py official repository unverified licence not identified · pointer only · d967dc5d7d0156c3 · report
Jn akirasosa/pytorch-dimenet/src/dimenet/modules/spherical_basis_layer.py community (archive-listed) unverified MIT (permissive) · 3275ba422c2a789a · report
Jn_zeros akirasosa/pytorch-dimenet/src/dimenet/modules/spherical_basis_layer.py community (archive-listed) unverified MIT (permissive) · ab2bb0b3e5be568c · report
calculate_interatomic_distances akirasosa/pytorch-dimenet/src/dimenet/functional.py community (archive-listed) unverified MIT (permissive) · 7e6b3b45707a5703 · report
calculate_neighbor_angles akirasosa/pytorch-dimenet/src/dimenet/functional.py community (archive-listed) unverified MIT (permissive) · c72e079ce4739527 · report
get_loader akirasosa/pytorch-dimenet/src/dimenet/loader.py community (archive-listed) unverified MIT (permissive) · 13510a6058a2e6e3 · report
spherical_bessel_formulas akirasosa/pytorch-dimenet/src/dimenet/modules/spherical_basis_layer.py community (archive-listed) unverified MIT (permissive) · dad5a152d86c0b92 · report
to_tensor akirasosa/pytorch-dimenet/src/dimenet/loader.py community (archive-listed) unverified MIT (permissive) · eb199682fba95610 · report

Tasks

Drug DiscoveryFormation Energy

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Drug Discovery QM9 DimeNet Error ratio 0.44 #7 of 11 Archive leaderboard report
Formation Energy QM9 DimeNet MAE 0.185 #9 of 18 Archive leaderboard report

Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.

Methods

MPNN

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