Search Results for author:
Found 5 papers, 0 papers with code
Neural P$^3$M: A Long-Range Interaction Modeling Enhancer for Geometric GNNs
Geometric graph neural networks (GNNs) have emerged as powerful tools for modeling molecular geometry.
Design of Ligand-Binding Proteins with Atomic Flow Matching
Designing novel proteins that bind to small molecules is a long-standing challenge in computational biology, with applications in developing catalysts, biosensors, and more.
Improving AlphaFlow for Efficient Protein Ensembles Generation
Investigating conformational landscapes of proteins is a crucial way to understand their biological functions and properties.
F$^3$low: Frame-to-Frame Coarse-grained Molecular Dynamics with SE(3) Guided Flow Matching
Molecular dynamics (MD) is a crucial technique for simulating biological systems, enabling the exploration of their dynamic nature and fostering an understanding of their functions and properties.
An ensemble of VisNet, Transformer-M, and pretraining models for molecular property prediction in OGB Large-Scale Challenge @ NeurIPS 2022
In the technical report, we provide our solution for OGB-LSC 2022 Graph Regression Task.
