Geometric Deep Learning for Drug Discovery and Single-Cell Omics
Date:
Comprehensive presentation on geometric deep learning applications in drug discovery and single-cell data analysis. Discussed graph neural networks for molecular property prediction, protein-ligand binding affinity estimation, and structure-based drug design. Presented novel approaches for learning on cellular graph representations, including cell-cell interaction networks and spatial transcriptomics data. Highlighted the integration of multi-modal biological data (genomics, proteomics, metabolomics) through geometric learning frameworks. Demonstrated state-of-the-art results on drug-target interaction prediction and cell type annotation tasks.