Curtis-Wu/Equivariant-Graph-Transformer
A deep neural network with hybrid architecture (EGNN + Transformer) for molecular properties prediction.
This project helps chemists and materials scientists predict molecular potential energy. You provide molecular structures, including atom types and their coordinates, and it outputs an estimated potential energy for that molecule. It's designed for researchers working with molecular simulations or drug discovery who need to quickly assess molecular stability and reactivity.
No commits in the last 6 months.
Use this if you need to accurately predict the potential energy of molecules based on their atomic structure to understand their behavior.
Not ideal if you are looking for a general-purpose graph transformer that operates on arbitrary graph data, as this is specifically tailored for molecular potential prediction.
Stars
23
Forks
3
Language
Jupyter Notebook
License
—
Category
Last pushed
Dec 09, 2023
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/transformers/Curtis-Wu/Equivariant-Graph-Transformer"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
SamsungSAILMontreal/nino
Code for "Accelerating Training with Neuron Interaction and Nowcasting Networks" [ICLR 2025]
graphdeeplearning/graphtransformer
Graph Transformer Architecture. Source code for "A Generalization of Transformer Networks to...
vijaydwivedi75/gnn-lspe
Source code for GNN-LSPE (Graph Neural Networks with Learnable Structural and Positional...
snap-stanford/relgt
Relational Graph Transformer
omron-sinicx/crystalframer
The official code respository for "Rethinking the role of frames for SE(3)-invariant crystal...