Diffusion-based Grap Generative Methods
This is the summation of all the methods, datasets, and other survey mentioned in our survey 'Diffusion-based Graph Generative Methods'.
Contents
Molecule generation
De novo molecule design
| Methods | Paper | Code | Methods | Paper | Code |
|---|---|---|---|---|---|
| DiGress (ICLR-23) | [paper] | [code] | MiDi (ICLR-23) | [paper] | [code] |
| CDGS (NeurIPS-22) | [paper] | [code] | GCDM (ICLR-23) | [paper] | [code] |
| EDM (ICML-22) | [paper] | [code] | Wu et al. (NeurIPS-22) | [paper] | - |
| MDM (AAAI-23) | [paper] | [code] | DiffLinker | [paper] | [code] |
| JODO | [paper] | [code] | SILVR | [paper] | - |
| HierDiff (ICML-23) | [paper] | [code] | GeoLDM (ICML-23) | [papar] | [code] |
| DiffMol (ICML-23) | [paper] | - | - | - | - |
Conformation design
| Methods | Paper | Code | Methods | Paper | Code |
|---|---|---|---|---|---|
| ConfGF (ICML-21) | [paper] | [code] | DGSM (NeurIPS-21) | [paper] | - |
| GeoDiff (ICLR-22) | [paper] | [code] | ColfNet (ICML-22) | [paper] | - |
| Torsion Diffusion (NeurIPS-22) | [paper] | [code] | DiffMD (AAAI-23) | [paper] | - |
| RINGER | [paper] | - | - | - | - |
De novo ligand design
| Methods | Paper | Code | Methods | Paper | Code |
|---|---|---|---|---|---|
| DiffBP | [paper] | - | DiffSBDD | [paper] | - |
| TargetDiff (ICLR-23) | [paper] | - | PMDM | [paper] | - |
| D3FG | [paper] | - | - | - | - |
Ligand docking
| Methods | Paper | Code |
|---|---|---|
| DIFFDOCK (NeurIPS-22) | [paper] | - |
| EDM-Dock (JCIM) | [paper] | [code] |
| DPL | [paper] | [code] |
| NeuralPLexer | [paper] | - |
Protein design
| Methods | Paper | Code | Methods | Paper | Code |
|---|---|---|---|---|---|
| DiffAb (NeurIPS-22) | [paper] | - | Anand | [paper] | - |
| PROTSEED (ICLR-23) | [paper] | - | ProteinSGM | [paper] | [code] |
| SMCDiff (ICLR-23) | [paper] | [code] | GraDe-IF | [paper] | - |
| EigenFold | [paper] | [code] | - | - | - |
Motion generation
Motion synthesis
| Methods | Paper | Code | Homepage | Methods | Paper | Code | Homepage |
|---|---|---|---|---|---|---|---|
| MotionDiffuse | [paper] | [code] | [homepage] | Modiff | [paper] | - | - |
| Ren et al. (ICASSP-23) | [paper] | - | - | FLAME (AAAI-23) | [paper] | - | - |
| MoFusion (CVPR-23) | [paper] | - | [homepage] | MDM (ICLR-23) | [paper] | [code] | [homepage] |
| MLD (CVPR-23) | [paper] | [code] | [homepage] | PriorMDM | [paper] | [code] | [homepage] |
| Alexanderson et al. (ACM Trans. Graph.) | [paper] | - | - | EDGE (CVPR-23) | [paper] | [code] | [homepage] |
| SceneDiffuser | [paper] | [code] | [homepage] | MoDi (CVPR-23) | [paper] | [code] | [homepage] |
| BiGraphDiff | [paper] | - | - | DiffuPose | [paper] | - | - |
Motion prediction
| Methods | Paper | Code | Homepage |
|---|---|---|---|
| Ahn et al. (ICRA-23) | [paper] | [code] | [homepage] |
| HumanMAC | [paper] | [code] | [homepage] |
| TCD (ICRA-23) | [paper] | [code] | - |
| DiffMotion | [paper] | - | - |
Others
| Methods | Paper | Code | Homepage | Methods | Paper | Code | Homepage |
|---|---|---|---|---|---|---|---|
| EDP-GNN | [paper] | - | - | GSDM | [paper] | - | - |
| NVDiff | [paper] | - | - | DPM-GSP | [paper] | - | - |
| DiffSTG | [paper] | - | - | DIFUSCO | [paper] | - | - |
| GraphGDP | [paper] | [code] | - | HouseDiffusion (CVPR-23) | [paper] | [code] | [homepage] |
| NAP | [paper] | - | [homepage] | EDGE (ICML-23) | [paper] | - | - |
| DruM | [paper] | - | - | DDM | [Paper] | - | - |
| DiffusionNAG | [paper] | - | - | TSDiff | [paper] | - | - |
| GraphArm (ICML-23) | [paper] | - | - | HGDM | [paper] | [code] | - |
| Lee et al. | [paper] | - | - | SaGess | [paper] | - | - |
| SLD | [paepr] | - | - | Diff-POI | [paper] | - | - |
| Brain Diffuser | [paper] | - | - | Lu et al. | [paper] | - | - |
Datasets
Molecule generation
| Methods | Paper | Source | Methods | Paper | Source |
|---|---|---|---|---|---|
| Zinc | [paper] | [source] | GEOM-QM9 | [paper] | [source] |
| GEOM-Drugs | [paper] | [source] | CrossDocked2020 | [paper] | [source] |
| BioLiP | [paper] | [source] | PDBBind | [paper] | [source] |
| SAbDab | [paper] | [source] | - | - | - |
Motion generation
| Methods | Paper | Source | Methods | Paper | Source |
|---|---|---|---|---|---|
| Human3.6M | [paper] | [source] | HumanEva-I | [paper] | [source] |
| HumanAct12 | [paper] | [source] | HumanML3D | [paper] | [source] |
| KIT | [paper] | [source] | BABEL | [paper] | [source] |
| UESTC | [paper] | [source] | 3DPW | [paper] | [source] |
| NTU RGB+D | [paper] | [source] | AIST++ | [paper] | [source] |
| TSG | [paper] | [source] | ZeroEGGS | [paper] | [source] |
Other surveys
| Paper | Url | Source |
|---|---|---|
| Diffusion Models: A Comprehensive Survey of Methods and Applications | [paper] | [source] |
| A Survey on Generative Diffusion Model | [paper] | [source] |
| Generative Diffusion Models on Graphs: Methods and Applications | [paper] | - |
| A Survey on Graph Diffusion Models: Generative AI in Science for Molecule, Protein and Material | [paper] | - |
| Graph-based Molecular Representation Learning | [paper] | - |
| Generative Models as an Emerging Paradigm in the Chemical Sciences | [paper] | - |
| A Survey on Deep Graph Generation: Methods and Applications (LoG-22) | [paper] | - |
| A Survey on Temporal Graph Representation Learning and Generative Modeling | [paper] | - |
| Human motion modeling with deep learning: A survey | [paper] | - |
| MolGenSurvey: A Systematic Survey in Machine Learning Models for Molecule Design | [paper] | - |