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Research Frontiers in Deep Learning Protein Fold Prediction

Neural network architectures designed to predict three-dimensional protein structures from amino acid sequences with high accuracy.

Implicit Geometry Learning Beyond Explicit Fold Spaces
Coevolutionary Signal Extraction in Language Model Embeddings
Protein Dynamics Prediction from Static Structure Inference
Cross-Species Fold Transfer and Domain Generalization
Uncertainty Quantification in Neural Fold Confidence
Ligand-Induced Conformational Landscapes from Sequence Alone
Interpretable Latent Spaces in Deep Folding Models
Few-Shot Fold Recognition in Orphan Protein Families
Physics-Informed Constraints in End-to-End Structure Prediction
Multimeric Assembly Prediction from Monomer Deep Learning

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