Research map / Graph learning / Graph algorithms and clustering
Predicting Organic Reaction Outcomes with Weisfeiler-Lehman Network
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Add global attention context to the reaction-center identifier (ablation) | WLN reaction-center model · USPTO-15K, K=8 | coverage: 86.1 -> 90.1 | Helped |
| Add global attention context to the reaction-center identifier (ablation) | WLN reaction-center model · USPTO, K=8 | coverage: 87.2 -> 92.0 | Helped |
| Use a WLDN difference graph rather than sum-pooling difference vectors (ablation) | Candidate ranking on USPTO-15K · USPTO-15K | P@1: 74.9 -> 76.7 | Helped |
| Add the true product when reaction-center candidate generation misses it (ablation) | WLDN candidate ranking · USPTO-15K | P@1: 76.7 -> 84.1 | Helped |
| Add the true product when reaction-center candidate generation misses it (ablation) | WLDN candidate ranking · USPTO | P@1: 79.6 -> 83.9 | Helped |
| Replace template generation and matching with reaction-center prediction and candidate enumeration | Template-free reaction prediction · USPTO candidate generation | speed: 140 times faster | Helped |
| Use the template-free model instead of the average chemist prediction | Reaction outcome prediction · USPTO test set, 80 reactions | accuracy: 48.2%; 69.1% | Helped |
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