 Cryptid pockets enable targeting of proteins currently considered undruggable because they lack pockets in their ground state structures. Here, the authors develop a graph neural network that accurately predicts cryptid pockets in static structures by training using molecular simulation data alone. This article was authored by Artumella, Michael Ward, Jonathan Borowski, and others.