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原文連結
論文資訊
- 類型：已發表論文
- 日期：2025-09-08
摘要
Molecular property prediction has become essential in accelerating advancements in drug discovery and materials science. Graph 神經 網絡s have recently demonstrated remarkable success in molecular representation learning; however, their broader adoption is impeded by two significant challenges: (1) data scarcity and constrained model generalization due to the expensive and time-consuming task of acquiring labeled data and (2) inadequate initial node and edge features that fail to incorporate comprehensive chemical domain knowledge, notably orbital 資訊. To address these limitations, we introduce a Knowledge-Guided Graph (KGG) framework employing self-supervised learning to pretrain models using orbital-level features in order to mitigate reliance on extensive labeled data sets. In addition, we p
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