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Prototype learning with structural-semantic alignment for interpretable molecular relational learning
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DOI：10.1016/j.knosys.2026.115460.png)
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Abstract
En 中文
• PSSA mitigates inconsistencies in MRL caused by conformation sensitivity. • ChemAlign integrates molecular information by structure-guided semantic learning. • RCProto enhances interpretability by prototype generation, fine-tuning, and matching. • PSSA outperforms comparative models in predictive performance and interpretability.
Keywords:
PSSA
ChemAlign
RCProto
prototype learning
molecular relational learning
Journal
K
IF:
7.6
Papers:
1.3W
Citations:
4.5W
Organization
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