Nat. Commun. | 突破分子活性预测盲区: 首个活性悬崖感知图学习框架
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TL;DR — A Nature Communications paper from Zhejiang University and Shenzhen Bay Laboratory introduces ACANET, the first "activity-cliff aware" graph learning framework, which adds a triplet contrastive loss to GNN training so structurally similar but functionally divergent molecules are separated in latent space. It matters because activity cliffs break the smoothness assumption behind QSAR/GNN models and are a key reason GNNs often lose to fingerprint-based ML in bioactivity prediction.
- The loss dynamically mines "high-value" cliff triplets (anchor, property-similar positive, property-divergent negative) from the current latent space with adaptive margins set by true property differences, rather than pre-labeling cliffs via a fixed fingerprint similarity threshold; two cliff thresholds plus a perception weight balance regression vs. cliff learning.
- Backbone-agnostic: applied to GCN, GIN, GAT, and PNA, it cut test error on PPARδ by ~4.6–6.6% and improved most of 30 mixed-scaffold datasets under fixed hyperparameters.
- Across 52 datasets, ACANET beat fingerprint SVM on average MAE (0.671→0.646 full test; 0.742→0.711 cliff subset), won 23/30 full and 21/30 cliff subsets, and held up on scaffold-holdout and strict similarity-excluded tests (28/30 targets).
- Latent-space label inconsistency dropped on BRAF (0.490 fingerprint → 0.290 plain GNN → 0.199 with cliff awareness, a 31.4% gain); the approach extends to matched-molecular-pair cliff classification and ADMET property-change prediction. Code: github.com/shenwanxiang/ACANet.
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Nat. Commun. | 突破分子活性预测盲区: 首个活性悬崖感知图学习框架
TL;DR — A Nature Communications paper from Zhejiang University and Shenzhen Bay Laboratory introduces ACANET, the first "activity-cliff aware" graph learning framework, which adds a triplet contrastive loss to GNN training so structurally similar but functionally divergent molecules are separated in latent space. It matters because activity cliffs break the smoothness assumption behind QSAR/GNN models and are a key reason GNNs often lose to fingerprint-based ML in bioactivity prediction.
- The loss dynamically mines "high-value" cliff triplets (anchor, property-similar positive, property-divergent negative) from the current latent space with adaptive margins set by true property differences, rather than pre-labeling cliffs via a fixed fingerprint similarity threshold; two cliff thresholds plus a perception weight balance regression vs. cliff learning.
- Backbone-agnostic: applied to GCN, GIN, GAT, and PNA, it cut test error on PPARδ by ~4.6–6.6% and improved most of 30 mixed-scaffold datasets under fixed hyperparameters.
- Across 52 datasets, ACANET beat fingerprint SVM on average MAE (0.671→0.646 full test; 0.742→0.711 cliff subset), won 23/30 full and 21/30 cliff subsets, and held up on scaffold-holdout and strict similarity-excluded tests (28/30 targets).
- Latent-space label inconsistency dropped on BRAF (0.490 fingerprint → 0.290 plain GNN → 0.199 with cliff awareness, a 31.4% gain); the approach extends to matched-molecular-pair cliff classification and ADMET property-change prediction. Code: github.com/shenwanxiang/ACANet.