419 citations · 932 across the 46 of their papers we have counts for
8 papers · 1 filter
ETDock: A Novel Equivariant Transformer for Protein-Ligand Docking
Yiqiang Yi, Xu Wan, Yatao Bian +2
Predicting the docking between proteins and ligands is a crucial and challenging task for drug discovery. However, traditional docking methods mainly rely on scoring functions, and…
SAILOR: Structural Augmentation Based Tail Node Representation Learning
Jie Liao, Jintang Li, Liang Chen +3
Graph Neural Networks (GNNs) have achieved state-of-the-art performance in representation learning for graphs recently. However, the effectiveness of GNNs, which capitalize on the…
SGFormer: Simplifying and Empowering Transformers for Large-Graph Representations
Qitian Wu, Wentao Zhao, Chenxiao Yang +5
Learning representations on large-sized graphs is a long-standing challenge due to the inter-dependence nature involved in massive data points. Transformers, as an emerging class o…
SyNDock: N Rigid Protein Docking via Learnable Group Synchronization
Yuanfeng Ji, Yatao Bian, Guoji Fu +2
The regulation of various cellular processes heavily relies on the protein complexes within a living cell, necessitating a comprehensive understanding of their three-dimensional st…
Reweighted Mixup for Subpopulation Shift
Zongbo Han, Zhipeng Liang, Fan Yang +8
Subpopulation shift exists widely in many real-world applications, which refers to the training and test distributions that contain the same subpopulation groups but with different…
Understanding and Improving Feature Learning for Out-of-Distribution Generalization
Yongqiang Chen, Wei Huang, Kaiwen Zhou +3
A common explanation for the failure of out-of-distribution (OOD) generalization is that the model trained with empirical risk minimization (ERM) learns spurious features instead o…
Fairness-guided Few-shot Prompting for Large Language Models
Huan Ma, Changqing Zhang, Yatao Bian +7
Large language models have demonstrated surprising ability to perform in-context learning, i.e., these models can be directly applied to solve numerous downstream tasks by conditio…
Activity Cliff Prediction: Dataset and Benchmark
Ziqiao Zhang, Bangyi Zhao, Ailin Xie +2
Activity cliffs (ACs), which are generally defined as pairs of structurally similar molecules that are active against the same bio-target but significantly different in the binding…