Papers › Neural Message Passing for Quantum Chemistry
Neural Message Passing for Quantum Chemistry
Justin Gilmer, Samuel S. Schoenholz, Patrick F. Riley, Oriol Vinyals, George E. Dahl
Supervised learning on molecules has incredible potential to be useful in chemistry, drug discovery, and materials science. Luckily, several promising and closely related neural network models invariant to molecular symmetries have already been described in the literature. These models learn a message passing algorithm and aggregation procedure to compute a function of their entire input graph. At this point, the next step is to find a particularly effective variant of this general approach and apply it to chemical prediction benchmarks until we either solve them or reach the limits of the approach. In this paper, we reformulate existing models into a single common framework we call Message Passing Neural Networks (MPNNs) and explore additional novel variations within this framework. Using MPNNs we demonstrate state of the art results on an important molecular property prediction benchmark; these results are strong enough that we believe future work should focus on datasets with larger molecules or more accurate ground truth labels.
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Code
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Code Syntology ran Syntology
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Drug Discovery | QM9 | MPNNs | Error ratio | 0.68 | #9 of 11 | Archive leaderboard | report |
| Formation Energy | QM9 | MPNN | MAE | 0.49 | #17 of 18 | Archive leaderboard | report |
| Graph Regression | Lipophilicity | MPNN | RMSE | 0.719 | #13 of 23 | Archive leaderboard | report |
| Graph Regression | ZINC 100k | MPNN | MAE | 0.288 | #5 of 8 | Archive leaderboard | report |
| Graph Regression | ZINC-500k | MPNN (sum) | MAE | 0.145 | #26 of 36 | Archive leaderboard | report |
| Graph Regression | ZINC-500k | MPNN (max) | MAE | 0.252 | #29 of 36 | Archive leaderboard | report |
| Node Classification | CiteSeer with Public Split: fixed 20 nodes per class | MPNN | Accuracy | 64.0 | #39 of 40 | 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
Introduced by this paper: MPNN
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