This paper proposes a new score-based approach to generate 3D molecules represented as atomic densities on regular grids.First, we train a denoising neural network that learns to map from a smooth distribution of noisy molecules to the distribution of real molecules.Then, we follow the _neural empirical Bayes_ framework [Saremi and Hyvarinen, 2019] in two steps: (i) sample noisy density grids from a smooth distribution via underdamped Langevin Markov chain Monte Carlo, and (ii) recover the "clean" molecule by denoising the noisy grid with a single step.Our method, VoxMol, generates molecules in a fundamentally different way than current state of the art (ie, diffusion models applied to atom point clouds). They differ in terms of the data representation, the noise model, the network architecture and the generative modeling algorithm.VoxMol achieves comparable results to state of the art on unconditional 3D molecule generation while being simpler to train and faster to generate molecules.