24 citations · 30 across the 3 of their papers we have counts for
3 papers
cs.LG2023★ 3 cited
Is Distance Matrix Enough for Geometric Deep Learning?
Zian Li, Xiyuan Wang, Yinan Huang +1
Graph Neural Networks (GNNs) are often used for tasks involving the 3D geometry of a given graph, such as molecular dynamics simulation. While incorporating Euclidean distance into…
cs.LG2022★ 3 cited
Boosting the Cycle Counting Power of Graph Neural Networks with I-GNNs
Yinan Huang, Xingang Peng, Jianzhu Ma +1
Message Passing Neural Networks (MPNNs) are a widely used class of Graph Neural Networks (GNNs). The limited representational power of MPNNs inspires the study of provably powerful…
cs.LG2022★ 24 cited
3DLinker: An E(3) Equivariant Variational Autoencoder for Molecular Linker Design
Yinan Huang, Xingang Peng, Jianzhu Ma +1
Deep learning has achieved tremendous success in designing novel chemical compounds with desirable pharmaceutical properties. In this work, we focus on a new type of drug design pr…