Antibiotic Discovery with Graph Deep Learning (Nature)

Graph neural networks: Trains GNNs on molecular graphs to predict antibiotic activity, with explainability layers that surface chemical substructures driving predictions.

Explainable discovery: Unlike black-box property predictors, the explanation module identifies substructures underlying antibiotic activity - a feature drug chemists can actually use.

New structural class: The discovered compounds belong to a novel structural class, not a variant of existing antibiotic scaffolds - an unusually strong generalization signal.

Real-world pipeline: Demonstrates end-to-end pipeline from GNN prediction to wet-lab validation, reinforcing explainable ML as a practical discovery tool for biomedicine.