3 papers
cs.LG2025
M-GLC: Motif-Driven Global-Local Context Graphs for Few-shot Molecular Property Prediction
Xiangyang Xu, Hongyang Gao
Molecular property prediction (MPP) is a cornerstone of drug discovery and materials science, yet conventional deep learning approaches depend on large labeled datasets that are of…
cs.LG2025
Flow-Matching Based Refiner for Molecular Conformer Generation
Xiangyang Xu, Hongyang Gao
Low-energy molecular conformers generation (MCG) is a foundational yet challenging problem in drug discovery. Denoising-based methods include diffusion and flow-matching methods th…
cs.LG2024
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…