4 papers
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…
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…
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…
Learning to Control the Smoothness of Graph Convolutional Network Features
Shih-Hsin Wang, Justin Baker, Cory Hauck +1
The pioneering work of Oono and Suzuki [ICLR, 2020] and Cai and Wang [arXiv:2006.13318] initializes the analysis of the smoothness of graph convolutional network (GCN) features. Th…