25 citations · 68 across the 8 of their papers we have counts for
9 papers
SemiRetro: Semi-template framework boosts deep retrosynthesis prediction
Zhangyang Gao, Cheng Tan, Lirong Wu +1
Recently, template-based (TB) and template-free (TF) molecule graph learning methods have shown promising results to retrosynthesis. TB methods are more accurate using pre-encoded…
AlphaDesign: A graph protein design method and benchmark on AlphaFoldDB
Zhangyang Gao, Cheng Tan, Stan Z. Li
While DeepMind has tentatively solved protein folding, its inverse problem -- protein design which predicts protein sequences from their 3D structures -- still faces significant ch…
Git: Clustering Based on Graph of Intensity Topology
Zhangyang Gao, Haitao Lin, Cheng Tan +2
\textbf{A}ccuracy, \textbf{R}obustness to noises and scales, \textbf{I}nterpretability, \textbf{S}peed, and \textbf{E}asy to use (ARISE) are crucial requirements of a good clusteri…
GraphMixup: Improving Class-Imbalanced Node Classification on Graphs by Self-supervised Context Prediction
Lirong Wu, Haitao Lin, Zhangyang Gao +2
Recent years have witnessed great success in handling node classification tasks with Graph Neural Networks (GNNs). However, most existing GNNs are based on the assumption that node…
Stability of Graph Convolutional Neural Networks to Stochastic Perturbations
Zhan Gao, Elvin Isufi, Alejandro Ribeiro
Graph convolutional neural networks (GCNNs) are nonlinear processing tools to learn representations from network data. A key property of GCNNs is their stability to graph perturbat…
Training Robust Graph Neural Networks with Topology Adaptive Edge Dropping
Zhan Gao, Subhrajit Bhattacharya, Leiming Zhang +3
Graph neural networks (GNNs) are processing architectures that exploit graph structural information to model representations from network data. Despite their success, GNNs suffer f…