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Ai Protein Structure Prediction

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Ai Protein Structure Prediction

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Research Frontiers in Equivariant Neural Networks for 3D Geometry

Design of SE(3)-equivariant and rotation-invariant neural networks that respect three-dimensional geometric constraints in protein structure prediction.

Symmetry-Preserving Learning in Folded Protein Landscapes

Equivariant Representations of Protein Dynamics and Conformational Space

Group-Theoretic Constraints on Neural Prediction of Tertiary Structure

Geometric Deep Learning for Protein-Ligand Binding Topology

Rotational Invariance in Backbone Angle Prediction Networks

Equivariant Message Passing Across Heterogeneous Protein Architectures

Symmetry Breaking and Loss of Prediction Fidelity in SE(3) Models

Manifold Learning of Protein Conformational Ensembles via Equivariance

Geometric Constraints for Coarse-Grained Protein Structure Networks

Translation and Rotation Equivariance in Multi-Chain Complex Prediction

All AI Protein Structure Prediction PhD categories