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Data Driven Interdisciplinary Science

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Data Driven Interdisciplinary Science

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Research Frontiers in Machine Learning for Protein Structure Prediction

Applies deep neural networks and graph-based algorithms to predict three-dimensional protein conformations from amino acid sequences and experimental data.

Implicit Symmetries and Equivariance in Protein Folding Networks

Language Models as Protein Structure Oracles

Thermodynamic Accessibility of Predicted Conformational Ensembles

Structure Prediction from Fragmented and Noisy Biological Data

Transferability Limits Across Protein Fold Space

Generative Models for Functional Protein Scaffolding

Interpretability at the Physics-Learning Boundary

Evolution-Aware Geometric Constraints in Deep Learning

Multi-Scale Temporal Dynamics in Structure Prediction

Protein Structure Prediction Under Extreme Conditions

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