52 citations · 64 across the 6 of their papers we have counts for
6 papers
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…
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…
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…
Reinforcement Learning for Personalized Drug Discovery and Design for Complex Diseases: A Systems Pharmacology Perspective
Ryan K. Tan, Yang Liu, Lei Xie
Many multi-genic systemic diseases such as neurological disorders, inflammatory diseases, and the majority of cancers do not have effective treatments yet. Reinforcement learning p…
Exploration of Dark Chemical Genomics Space via Portal Learning: Applied to Targeting the Undruggable Genome and COVID-19 Anti-Infective Polypharmacology
Tian Cai, Li Xie, Muge Chen +6
Advances in biomedicine are largely fueled by exploring uncharted territories of human biology. Machine learning can both enable and accelerate discovery, but faces a fundamental h…
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…