515 citations · 755 across the 20 of their papers we have counts for
5 papers · 1 filter
DiffBP: Generative Diffusion of 3D Molecules for Target Protein Binding
Haitao Lin, Yufei Huang, Odin Zhang +7
Generating molecules that bind to specific proteins is an important but challenging task in drug discovery. Previous works usually generate atoms in an auto-regressive way, where e…
GraphFM: Improving Large-Scale GNN Training via Feature Momentum
Haiyang Yu, Limei Wang, Bokun Wang +3
Training of graph neural networks (GNNs) for large-scale node classification is challenging. A key difficulty lies in obtaining accurate hidden node representations while avoiding…
Empowering GNNs via Edge-Aware Weisfeiler-Leman Algorithm
Meng Liu, Haiyang Yu, Shuiwang Ji
Message passing graph neural networks (GNNs) are known to have their expressiveness upper-bounded by 1-dimensional Weisfeiler-Leman (1-WL) algorithm. To achieve more powerful GNNs,…
Generating 3D Molecules for Target Protein Binding
Meng Liu, Youzhi Luo, Kanji Uchino +2
A fundamental problem in drug discovery is to design molecules that bind to specific proteins. To tackle this problem using machine learning methods, here we propose a novel and ef…
Neighbor2Seq: Deep Learning on Massive Graphs by Transforming Neighbors to Sequences
Meng Liu, Shuiwang Ji
Modern graph neural networks (GNNs) use a message passing scheme and have achieved great success in many fields. However, this recursive design inherently leads to excessive comput…