UC San Diego researchers developed MitoSpace, an unsupervised AI model trained on 40,000 4D cellular videos to classify drugs by mechanism with 75% accuracy—19 points higher than 2D imaging methods. A parallel physics-based digital twin approach uses rule-based simulation to replicate real cellular behavior. Both methods could accelerate drug screening and enable broader cell biology research beyond mitochondria alone.