20 citations · 29 across the 4 of their papers we have counts for
4 papers · 1 filter
Drug Synergistic Combinations Predictions via Large-Scale Pre-Training and Graph Structure Learning
Zhihang Hu, Qinze Yu, Yucheng Guo +5
Drug combination therapy is a well-established strategy for disease treatment with better effectiveness and less safety degradation. However, identifying novel drug combinations th…
Uncovering the Structural Fairness in Graph Contrastive Learning
Ruijia Wang, Xiao Wang, Chuan Shi +1
Recent studies show that graph convolutional network (GCN) often performs worse for low-degree nodes, exhibiting the so-called structural unfairness for graphs with long-tailed deg…
Graph Condensation via Receptive Field Distribution Matching
Mengyang Liu, Shanchuan Li, Xinshi Chen +1
Graph neural networks (GNNs) enable the analysis of graphs using deep learning, with promising results in capturing structured information in graphs. This paper focuses on creating…
Molecular Attributes Transfer from Non-Parallel Data
Shuangjia Zheng, Ying Song, Zhang Pan +3
Optimizing chemical molecules for desired properties lies at the core of drug development. Despite initial successes made by deep generative models and reinforcement learning metho…