30 citations · 99 across the 14 of their papers we have counts for
14 papers
Self-Consistency Training for Density-Functional-Theory Hamiltonian Prediction
He Zhang, Chang Liu, Zun Wang +5
Predicting the mean-field Hamiltonian matrix in density functional theory is a fundamental formulation to leverage machine learning for solving molecular science problems. Yet, its…
Towards Predicting Equilibrium Distributions for Molecular Systems with Deep Learning
Shuxin Zheng, Jiyan He, Chang Liu +15
Advances in deep learning have greatly improved structure prediction of molecules. However, many macroscopic observations that are important for real-world applications are not fun…
Long-Short-Range Message-Passing: A Physics-Informed Framework to Capture Non-Local Interaction for Scalable Molecular Dynamics Simulation
Yunyang Li, Yusong Wang, Lin Huang +7
Computational simulation of chemical and biological systems using ab initio molecular dynamics has been a challenge over decades. Researchers have attempted to address the problem…
An ensemble of VisNet, Transformer-M, and pretraining models for molecular property prediction in OGB Large-Scale Challenge @ NeurIPS 2022
Yusong Wang, Shaoning Li, Zun Wang +4
In the technical report, we provide our solution for OGB-LSC 2022 Graph Regression Task. The target of this task is to predict the quantum chemical property, HOMO-LUMO gap for a gi…
Invertible Rescaling Network and Its Extensions
Mingqing Xiao, Shuxin Zheng, Chang Liu +2
Image rescaling is a commonly used bidirectional operation, which first downscales high-resolution images to fit various display screens or to be storage- and bandwidth-friendly, a…
ViSNet: an equivariant geometry-enhanced graph neural network with vector-scalar interactive message passing for molecules
Yusong Wang, Shaoning Li, Xinheng He +6
Geometric deep learning has been revolutionizing the molecular modeling field. Despite the state-of-the-art neural network models are approaching ab initio accuracy for molecular p…
One Transformer Can Understand Both 2D & 3D Molecular Data
Shengjie Luo, Tianlang Chen, Yixian Xu +4
Unlike vision and language data which usually has a unique format, molecules can naturally be characterized using different chemical formulations. One can view a molecule as a 2D g…
Tailoring Molecules for Protein Pockets: a Transformer-based Generative Solution for Structured-based Drug Design
Kehan Wu, Yingce Xia, Yang Fan +7
Structure-based drug design is drawing growing attentions in computer-aided drug discovery. Compared with the virtual screening approach where a pre-defined library of compounds ar…
SSM-DTA: Breaking the Barriers of Data Scarcity in Drug-Target Affinity Prediction
Qizhi Pei, Lijun Wu, Jinhua Zhu +6
Accurate prediction of Drug-Target Affinity (DTA) is of vital importance in early-stage drug discovery, facilitating the identification of drugs that can effectively interact with…
Driving atomic structures of molecules, crystals, and complex systems with local similarity kernels
Ziheng Lu, Wenlei Shi, Lixin Sun +2
Accessing structures of molecules, crystals, and complex interfaces with atomic level details is vital to the understanding and engineering of materials, chemical reactions, and bi…
Multi-View Substructure Learning for Drug-Drug Interaction Prediction
Zimeng Li, Shichao Zhu, Bin Shao +3
Drug-drug interaction (DDI) prediction provides a drug combination strategy for systemically effective treatment. Previous studies usually model drug information constrained on a s…
An Empirical Study of Graphormer on Large-Scale Molecular Modeling Datasets
Yu Shi, Shuxin Zheng, Guolin Ke +7
This technical note describes the recent updates of Graphormer, including architecture design modifications, and the adaption to 3D molecular dynamics simulation. The "Graphormer-V…
Benchmarking Graphormer on Large-Scale Molecular Modeling Datasets
Yu Shi, Shuxin Zheng, Guolin Ke +7
This technical note describes the recent updates of Graphormer, including architecture design modifications, and the adaption to 3D molecular dynamics simulation. With these simple…
Direct Molecular Conformation Generation
Jinhua Zhu, Yingce Xia, Chang Liu +9
Molecular conformation generation aims to generate three-dimensional coordinates of all the atoms in a molecule and is an important task in bioinformatics and pharmacology. Previou…