Training neural network potentials
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Updated
Aug 14, 2026 - Python
Training neural network potentials
EquiDock: geometric deep learning for fast rigid 3D protein-protein docking
We would like to maintain a list of resources which aim to solve molecular docking and other closely related tasks.
[NAACL 2022] Robust (Controlled) Table-to-Text Generation with Structure-Aware Equivariance Learning.
Learning to design protein-protein interactions with enhanced generalization (ICLR 2024)
"Exploring Complementary Strengths of Invariant and Equivariant Representations for Few-Shot Learning" by Mamshad Nayeem Rizve, Salman Khan, Fahad Shahbaz Khan, Mubarak Shah (CVPR 2021)
Official PyTorch implementation of Möbius Convolutions for Spherical CNNs [SIGGRAPH 2022].
Multimodal Pretraining for Unsupervised Protein Representation Learning
A Multi-Operator Equivariant Framework for High-Performance Machine Learning Force Fields, supporting External Fields embedding and Physical Tensors prediction.
Integrating Neural Ordinary Differential Equations, the Method of Lines, and Graph Neural Networks
Rotationally Equivariant Hypergraph Neural Networks (EquiHGNN)
TESGNN: 3D Temporal Equivariant Scene Graph Neural Networks (published at TMLR)
Implementation of "Denoise Pretraining on Non-equilibrium Molecular Conformations for Accurate and Transferable Neural Potentials" in PyTorch.
E(3)-Equivariant Mesh Neural Networks (AISTATS 2024)
Official code for Learning Temporally Equivariance for Degenerative Disease Progression in OCT by Predicting Future Representations (MICCAI'24)
An implementation of the Atiyah-Bott formula for the moduli space of genus 0 stable maps.
Official implementation of "A Deep Learning Model of Mental Rotation Informed by Interactive VR Experiments"
Implementation of paper: Equivariant Learning for Out-of-Distribution Cold-start Recommendation. (backbone model CLCRec) (MM'23)
Code repository for ICML 2026 submission "Representation Learning for Equivariant Inference with Guarantees".
Torch implementation of Marc Finzi's Equivariant MLP
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