3 papers
cs.LG2026
Towards Multiscale Graph-based Protein Learning with Geometric Secondary Structural Motifs
Shih-Hsin Wang, Yuhao Huang, Taos Transue +4
Graph neural networks (GNNs) have emerged as powerful tools for learning protein structures by capturing spatial relationships at the residue level. However, existing GNN-based met…
cs.LG2025
Conformational Rank Conditioned Committees for Machine Learning-Assisted Directed Evolution
Mia Adler, Carrie Liang, Brian Peng +5
Machine Learning-assisted directed evolution (MLDE) is a powerful tool for efficiently navigating antibody fitness landscapes. Many structure-aware MLDE pipelines rely on a single…
cs.LG2025
Boltzmann Graph Ensemble Embeddings for Aptamer Libraries
Starlika Bauskar, Jade Jiao, Narayanan Kannan +5
Machine-learning methods in biochemistry commonly represent molecules as graphs of pairwise intermolecular interactions for property and structure predictions. Most methods operate…