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Computational Biology

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Research Frontiers in Deep Learning for Protein Structure Prediction

Development of neural network architectures for predicting three-dimensional protein structures from amino acid sequences with high accuracy and computational efficiency.

Protein Folding in Non-Euclidean Geometric Spaces

Equivariant Neural Networks and Symmetry Preservation

Language Models as Protein Structure Oracles

Coevolution-Informed Deep Learning Architectures

Inverse Folding: Sequence Design from Structure

Protein Dynamics and Conformational Ensembles

Weak Supervision and Sparse Experimental Data

Thermodynamic Validity in Deep Structure Prediction

Membrane Proteins and Lipid-Aware Learning

Structure Prediction at Genomic Scale

All Computational Biology PhD categories