Research map / Probabilistic and scientific modeling / Molecules and proteins
Generative modeling for protein structures
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| represent backbone as C-alpha pairwise distance maps instead of torsion angles, full-atom maps or 3D voxels | GAN protein structure generation · 64-residue structure generation | structural plausibility (qualitative) | Helped |
| GAN inpainting vs supervised autoencoders and RosettaRemodel | protein loop inpainting · 64-residue inpainting | rigid-body alignment error to native | Hurt |
| train autoencoder on random-length corruptions instead of fixed 10-residue | supervised autoencoder inpainting · 64-residue inpainting | solution realism (qualitative) | Helped |
| fold with ADMM + C-alpha trace instead of Rosetta fragment sampling | structure recovery · generated maps | runtime vs local structure correctness | Mixed |
Closest papers by meaning
- Proteina: Scaling Flow-based Protein Structure Generative Models (ICLR 2025 · similarity 0.88)
- Proteus: Exploring Protein Structure Generation for Enhanced Designability and Efficiency (ICML 2024 · similarity 0.86)
- Evaluating Representation Learning on the Protein Structure Universe (ICLR 2024 · similarity 0.86)
- Generating Highly Designable Proteins with Geometric Algebra Flow Matching (NeurIPS 2024 · similarity 0.86)
- Learning conformational ensembles of proteins based on backbone geometry (NeurIPS 2025 · similarity 0.86)
- Protein 3D Graph Structure Learning for Robust Structure-Based Protein Property Prediction (AAAI 2024 · similarity 0.85)
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