  We present VoxBind, a new score-based generative model for 3D molecules
conditioned on protein structures. Our approach represents molecules as 3D
atomic density grids and leverages a 3D voxel-denoising network for learning
and generation. We extend the neural empirical Bayes formalism (Saremi &
Hyvarinen, 2019) to the conditional setting and generate structure-conditioned
molecules with a two-step procedure: (i) sample noisy molecules from the
Gaussian-smoothed conditional distribution with underdamped Langevin MCMC using
the learned score function and (ii) estimate clean molecules from the noisy
samples with single-step denoising. Compared to the current state of the art,
our model is simpler to train, significantly faster to sample from, and
achieves better results on extensive in silico benchmarks -- the generated
molecules are more diverse, exhibit fewer steric clashes, and bind with higher
affinity to protein pockets. The code is available at
https://github.com/genentech/voxbind/.
