Training neural network potentials
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Updated
Dec 3, 2024 - 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
Integrating Neural Ordinary Differential Equations, the Method of Lines, and Graph Neural Networks
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)
TESGNN: 3D Temporal Equivariant Scene Graph Neural Networks
An implementation of the Atiyah-Bott formula for the moduli space of genus 0 stable maps.
Implementation of paper: Equivariant Learning for Out-of-Distribution Cold-start Recommendation. (backbone model CLCRec) (MM'23)
Official code for Learning Temporally Equivariance for Degenerative Disease Progression in OCT by Predicting Future Representations (MICCAI'24)
Official Implementation of "Towards Self-Supervised Gaze Estimation"
Torch implementation of Marc Finzi's Equivariant MLP
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