most citedA Universal Framework for Accurate and Efficient Geometric Deep Learning of Molecular Systems

25 citations · 38 across the 5 of their papers we have counts for

collaborators

5 papers

q-bio.QM2023★ 1 cited

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…

cs.LG2023★ 25 cited

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…

cs.LG2023

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…

cs.LG2022★ 6 cited

Physics-aware Graph Neural Network for Accurate RNA 3D Structure Prediction

Shuo Zhang, Yang Liu, Lei Xie

Biological functions of RNAs are determined by their three-dimensional (3D) structures. Thus, given the limited number of experimentally determined RNA structures, the prediction o…

q-bio.BM2022★ 6 cited

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…