57 citations · 208 across the 13 of their papers we have counts for
13 papers
Protein Representation Learning with Secondary-Structure and Energy-Filtered Hydrogen-Bond Graphs
Mohamed Mouhajir, Limei Wang, El Houcine Bergou +3
Graph-based representations are widely used in protein modeling, yet many existing approaches rely primarily on sequence adjacency or geometric proximity, which only partially refl…
Geometry Informed Tokenization of Molecules for Language Model Generation
Xiner Li, Limei Wang, Youzhi Luo +5
We consider molecule generation in 3D space using language models (LMs), which requires discrete tokenization of 3D molecular geometries. Although tokenization of molecular graphs…
Artificial Intelligence for Science in Quantum, Atomistic, and Continuum Systems
Xuan Zhang, Limei Wang, Jacob Helwig +60
Advances in artificial intelligence (AI) are fueling a new paradigm of discoveries in natural sciences. Today, AI has started to advance natural sciences by improving, accelerating…
A Latent Diffusion Model for Protein Structure Generation
Cong Fu, Keqiang Yan, Limei Wang +7
Proteins are complex biomolecules that perform a variety of crucial functions within living organisms. Designing and generating novel proteins can pave the way for many future synt…
A new perspective on building efficient and expressive 3D equivariant graph neural networks
Weitao Du, Yuanqi Du, Limei Wang +5
Geometric deep learning enables the encoding of physical symmetries in modeling 3D objects. Despite rapid progress in encoding 3D symmetries into Graph Neural Networks (GNNs), a co…
Learning Hierarchical Protein Representations via Complete 3D Graph Networks
Limei Wang, Haoran Liu, Yi Liu +2
We consider representation learning for proteins with 3D structures. We build 3D graphs based on protein structures and develop graph networks to learn their representations. Depen…
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…
ComENet: Towards Complete and Efficient Message Passing for 3D Molecular Graphs
Limei Wang, Yi Liu, Yuchao Lin +2
Many real-world data can be modeled as 3D graphs, but learning representations that incorporates 3D information completely and efficiently is challenging. Existing methods either u…
GOOD: A Graph Out-of-Distribution Benchmark
Shurui Gui, Xiner Li, Limei Wang +1
Out-of-distribution (OOD) learning deals with scenarios in which training and test data follow different distributions. Although general OOD problems have been intensively studied…
Fast Quantum Property Prediction via Deeper 2D and 3D Graph Networks
Meng Liu, Cong Fu, Xuan Zhang +7
Molecular property prediction is gaining increasing attention due to its diverse applications. One task of particular interests and importance is to predict quantum chemical proper…
DIG: A Turnkey Library for Diving into Graph Deep Learning Research
Meng Liu, Youzhi Luo, Limei Wang +13
Although there exist several libraries for deep learning on graphs, they are aiming at implementing basic operations for graph deep learning. In the research community, implementin…
Spherical Message Passing for 3D Graph Networks
Yi Liu, Limei Wang, Meng Liu +3
We consider representation learning of 3D molecular graphs in which each atom is associated with a spatial position in 3D. This is an under-explored area of research, and a princip…
Advanced Graph and Sequence Neural Networks for Molecular Property Prediction and Drug Discovery
Zhengyang Wang, Meng Liu, Youzhi Luo +8
Properties of molecules are indicative of their functions and thus are useful in many applications. With the advances of deep learning methods, computational approaches for predict…