arXiv AI
Hyper-Fold: Exploring the Expressive Limit of Sequence-Geometry Learning for Proteins via Hypergraph Modeling
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PRIME: Protein Representation via Physics-Informed Multiscale Equivariant Hierarchies
arXiv:2605. 01625v3 Announce Type: replace Abstract: Proteins are inherently multiscale physical systems whose functional properties emerge from coordinated structural organization across multiple spatial resolutions, ranging from atomic interactions to global fold topology.
arXiv AI
2d ago
SymFold: Synergizing Evolutionary and Structural Priors for Accurate Protein Inverse Folding
SymFold introduces a symmetric dual‑path architecture that combines protein language models (PLMs) and multimodal protein language models (MPLMs) to iteratively guide protein sequence generation for inverse folding. By leveraging pretrained sequence evolution knowledge from PLMs and structural knowledge from MPLMs, the method improves upon the traditional serial pipeline where structure encoders produce coarse sequences refined by PLMs. Experiments on standard inverse‑folding benchmarks show state‑of‑the‑art performance, and ablation studies confirm the effectiveness of the symmetric design.
arXiv AI
Aug 18
Protein Structure Prediction: From Evolutionary Constraints to Generative Modeling
arXiv:2608. 16094v1 Announce Type: new Abstract: Accurate protein structure prediction is fundamental to structural biology because protein structure underlies molecular function and provides a basis for mechanistic interpretation.
arXiv Machine Learning
Jun 15
Curvature-Informed Potential Energy Surface for Protein-Ligand Binding Affinity Prediction
arXiv:2606. 14217v1 Announce Type: new Abstract: Accurate prediction of protein-ligand binding affinity is essential for structure-based drug discovery.
arXiv Machine Learning
Jun 25
Two Stages of Folding: Convergent Mechanisms in AI Protein Folding Trunks
arXiv:2602. 06020v3 Announce Type: replace Abstract: How do protein structure prediction models fold proteins?
arXiv AI
Aug 12
Proteo-R1: Reasoning Foundation Models for De Novo Protein Design
arXiv:2605. 02937v2 Announce Type: replace-cross Abstract: Deep learning in de novo protein design has achieved atomic-level fidelity.