Life is physics and chemistry in action. While molecular simulations of systems as complex as whole cells are now within reach, predicting chemical reactivity on relevant time and length scales remains a challenge. I will present our recent work towards bringing action – here: chemistry – to classical simulations and molecular design through machine learning. Among others, we substitute costly quantum mechanical calculations with a graph neural network-based emulator. Our framework can deal with the plethora of life’s chemistry amidst the ‘jiggling and wiggling’ of biomolecules. Importantly, we also uncover unexpected biomolecular processes that we in turn put to test in experiments. Finally, I will demonstrate how we harness a flow-matching model to predict biomolecular dynamics. Our method paves the way for generating novel flexible and functional proteins.