Toward Better Geometric Representations for Molecule Generative Models
Fuente:
arXiv
Saved in:
| Main Authors: | Yan, Shaoheng, Li, Zian, Zhou, Cai, Huang, Qiaojing, Liu, Kai, Zhang, Muhan |
|---|---|
| Format: | Preprint |
| Published: |
2026
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
GeoRecon: Graph-Level Representation Learning for 3D Molecules via Reconstruction-Based Pretraining
by: Yan, Shaoheng, et al.
Published: (2025)
by: Yan, Shaoheng, et al.
Published: (2025)
FlashMol: High-Quality Molecule Generation in as Few as Four Steps
by: Wei, Xinyuan, et al.
Published: (2026)
by: Wei, Xinyuan, et al.
Published: (2026)
Geometric Representation Condition Improves Equivariant Molecule Generation
by: Li, Zian, et al.
Published: (2024)
by: Li, Zian, et al.
Published: (2024)
Is Distance Matrix Enough for Geometric Deep Learning?
by: Li, Zian, et al.
Published: (2023)
by: Li, Zian, et al.
Published: (2023)
On the Completeness of Invariant Geometric Deep Learning Models
by: Li, Zian, et al.
Published: (2024)
by: Li, Zian, et al.
Published: (2024)
CanvasMAR: Improving Masked Autoregressive Video Prediction With Canvas
by: Li, Zian, et al.
Published: (2025)
by: Li, Zian, et al.
Published: (2025)
Towards Stable, Globally Expressive Graph Representations with Laplacian Eigenvectors
by: Zhou, Junru, et al.
Published: (2024)
by: Zhou, Junru, et al.
Published: (2024)
VecMol: Vector-Field Representations for 3D Molecule Generation
by: Hua, Yuchen, et al.
Published: (2026)
by: Hua, Yuchen, et al.
Published: (2026)
Unifying Generation and Prediction on Graphs with Latent Graph Diffusion
by: Zhou, Cai, et al.
Published: (2024)
by: Zhou, Cai, et al.
Published: (2024)
Rethinking Diffusion Models with Symmetries through Canonicalization with Applications to Molecular Graph Generation
by: Zhou, Cai, et al.
Published: (2026)
by: Zhou, Cai, et al.
Published: (2026)
OCN: Effectively Utilizing Higher-Order Common Neighbors for Better Link Prediction
by: Wang, Juntong, et al.
Published: (2025)
by: Wang, Juntong, et al.
Published: (2025)
Round-trip Reinforcement Learning: Self-Consistent Training for Better Chemical LLMs
by: Kong, Lecheng, et al.
Published: (2025)
by: Kong, Lecheng, et al.
Published: (2025)
Exploring Representation-Aligned Latent Space for Better Generation
by: Xu, Wanghan, et al.
Published: (2025)
by: Xu, Wanghan, et al.
Published: (2025)
Fine-Grained Expressive Power of Weisfeiler-Leman: A Homomorphism Counting Perspective
by: Zhou, Junru, et al.
Published: (2024)
by: Zhou, Junru, et al.
Published: (2024)
Do Graph Diffusion Models Accurately Capture and Generate Substructure Distributions?
by: Wang, Xiyuan, et al.
Published: (2025)
by: Wang, Xiyuan, et al.
Published: (2025)
On Lexical Invariance on Multisets and Graphs
by: Zhang, Muhan
Published: (2024)
by: Zhang, Muhan
Published: (2024)
GOFA: A Generative One-For-All Model for Joint Graph Language Modeling
by: Kong, Lecheng, et al.
Published: (2024)
by: Kong, Lecheng, et al.
Published: (2024)
UniMoMo: Unified Generative Modeling of 3D Molecules for De Novo Binder Design
by: Kong, Xiangzhe, et al.
Published: (2025)
by: Kong, Xiangzhe, et al.
Published: (2025)
Molecule Generation for Target Protein Binding with Hierarchical Consistency Diffusion Model
by: Li, Guanlue, et al.
Published: (2025)
by: Li, Guanlue, et al.
Published: (2025)
Towards Understanding Why FixMatch Generalizes Better Than Supervised Learning
by: Li, Jingyang, et al.
Published: (2024)
by: Li, Jingyang, et al.
Published: (2024)
AURORA: Contextual Orthogonalization for Geometric Representation Learning in Healthcare Foundation Models
by: Zhang, Yuanyun, et al.
Published: (2026)
by: Zhang, Yuanyun, et al.
Published: (2026)
One for All: Towards Training One Graph Model for All Classification Tasks
by: Liu, Hao, et al.
Published: (2023)
by: Liu, Hao, et al.
Published: (2023)
Towards Better Statistical Understanding of Watermarking LLMs
by: Cai, Zhongze, et al.
Published: (2024)
by: Cai, Zhongze, et al.
Published: (2024)
Towards Better Understanding of In-Context Learning Ability from In-Context Uncertainty Quantification
by: Liu, Shang, et al.
Published: (2024)
by: Liu, Shang, et al.
Published: (2024)
DrugLLM: Open Large Language Model for Few-shot Molecule Generation
by: Liu, Xianggen, et al.
Published: (2024)
by: Liu, Xianggen, et al.
Published: (2024)
Using Random Noise Equivariantly to Boost Graph Neural Networks Universally
by: Wang, Xiyuan, et al.
Published: (2025)
by: Wang, Xiyuan, et al.
Published: (2025)
An Efficient Subgraph GNN with Provable Substructure Counting Power
by: Yan, Zuoyu, et al.
Published: (2023)
by: Yan, Zuoyu, et al.
Published: (2023)
Geometric-Facilitated Denoising Diffusion Model for 3D Molecule Generation
by: Xu, Can, et al.
Published: (2024)
by: Xu, Can, et al.
Published: (2024)
Diffusion Models at the Drug Discovery Frontier: A Review on Generating Small Molecules versus Therapeutic Peptides
by: Wang, Yiquan, et al.
Published: (2025)
by: Wang, Yiquan, et al.
Published: (2025)
Bridging the Gap between Chemical Reaction Pretraining and Conditional Molecule Generation with a Unified Model
by: Qiang, Bo, et al.
Published: (2023)
by: Qiang, Bo, et al.
Published: (2023)
Graph as Point Set
by: Wang, Xiyuan, et al.
Published: (2024)
by: Wang, Xiyuan, et al.
Published: (2024)
Improving Graph Neural Networks on Multi-node Tasks with the Labeling Trick
by: Wang, Xiyuan, et al.
Published: (2023)
by: Wang, Xiyuan, et al.
Published: (2023)
Graph is a Natural Regularization: Revisiting Vector Quantization for Graph Representation Learning
by: Zhai, Zian, et al.
Published: (2025)
by: Zhai, Zian, et al.
Published: (2025)
The Expressivity Boundary of Probabilistic Circuits: A Comparison with Large Language Models
by: Zhao, Zhiyu, et al.
Published: (2026)
by: Zhao, Zhiyu, et al.
Published: (2026)
Accelerating 3D Molecule Generation via Jointly Geometric Optimal Transport
by: Hong, Haokai, et al.
Published: (2024)
by: Hong, Haokai, et al.
Published: (2024)
Representation Learning of Geometric Trees
by: Zhang, Zheng, et al.
Published: (2024)
by: Zhang, Zheng, et al.
Published: (2024)
Learning Disentangled Equivariant Representation for Explicitly Controllable 3D Molecule Generation
by: Liu, Haoran, et al.
Published: (2024)
by: Liu, Haoran, et al.
Published: (2024)
Towards Better Generalization and Interpretability in Unsupervised Concept-Based Models
by: De Santis, Francesco, et al.
Published: (2025)
by: De Santis, Francesco, et al.
Published: (2025)
Smaller But Better: Unifying Layout Generation with Smaller Large Language Models
by: Zhang, Peirong, et al.
Published: (2025)
by: Zhang, Peirong, et al.
Published: (2025)
Generating $π$-Functional Molecules Using STGG+ with Active Learning
by: Jolicoeur-Martineau, Alexia, et al.
Published: (2025)
by: Jolicoeur-Martineau, Alexia, et al.
Published: (2025)
Similar Items
-
GeoRecon: Graph-Level Representation Learning for 3D Molecules via Reconstruction-Based Pretraining
by: Yan, Shaoheng, et al.
Published: (2025) -
FlashMol: High-Quality Molecule Generation in as Few as Four Steps
by: Wei, Xinyuan, et al.
Published: (2026) -
Geometric Representation Condition Improves Equivariant Molecule Generation
by: Li, Zian, et al.
Published: (2024) -
Is Distance Matrix Enough for Geometric Deep Learning?
by: Li, Zian, et al.
Published: (2023) -
On the Completeness of Invariant Geometric Deep Learning Models
by: Li, Zian, et al.
Published: (2024)