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20232025
most citedG2T-LLM: Graph-to-Tree Text Encoding for Molecule Generation with Fine-Tuned Large Language Models

3 citations · 7 across the 4 of their papers we have counts for

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cs.LG2024★ 3 cited

G2T-LLM: Graph-to-Tree Text Encoding for Molecule Generation with Fine-Tuned Large Language Models

Zhaoning Yu, Xiangyang Xu, Hongyang Gao

We introduce G2T-LLM, a novel approach for molecule generation that uses graph-to-tree text encoding to transform graph-based molecular structures into a hierarchical text format o…

cs.LG2024★ 2 cited

MAGE: Model-Level Graph Neural Networks Explanations via Motif-based Graph Generation

Zhaoning Yu, Hongyang Gao

Graph Neural Networks (GNNs) have shown remarkable success in molecular tasks, yet their interpretability remains challenging. Traditional model-level explanation methods like XGNN…