52 citations · 132 across the 6 of their papers we have counts for
6 papers
Protein Language Model-Powered 3D Ligand Binding Site Prediction from Protein Sequence
Shuo Zhang, Lei Xie
Prediction of ligand binding sites of proteins is a fundamental and important task for understanding the function of proteins and screening potential drugs. Most existing methods r…
A Universal Framework for Accurate and Efficient Geometric Deep Learning of Molecular Systems
Shuo Zhang, Yang Liu, Lei Xie
Molecular sciences address a wide range of problems involving molecules of different types and sizes and their complexes. Recently, geometric deep learning, especially Graph Neural…
Learning Universal and Robust 3D Molecular Representations with Graph Convolutional Networks
Shuo Zhang, Yang Liu, Li Xie +1
To learn accurate representations of molecules, it is essential to consider both chemical and geometric features. To encode geometric information, many descriptors have been propos…
Efficient and Accurate Physics-aware Multiplex Graph Neural Networks for 3D Small Molecules and Macromolecule Complexes
Shuo Zhang, Yang Liu, Lei Xie
Recent advances in applying Graph Neural Networks (GNNs) to molecular science have showcased the power of learning three-dimensional (3D) structure representations with GNNs. Howev…
Molecular Mechanics-Driven Graph Neural Network with Multiplex Graph for Molecular Structures
Shuo Zhang, Yang Liu, Lei Xie
The prediction of physicochemical properties from molecular structures is a crucial task for artificial intelligence aided molecular design. A growing number of Graph Neural Networ…
Improving Attention Mechanism in Graph Neural Networks via Cardinality Preservation
Shuo Zhang, Lei Xie
Graph Neural Networks (GNNs) are powerful to learn the representation of graph-structured data. Most of the GNNs use the message-passing scheme, where the embedding of a node is it…