  We present NEBULA, the first latent 3D generative model for scalable
generation of large molecular libraries around a seed compound of interest.
Such libraries are crucial for scientific discovery, but it remains challenging
to generate large numbers of high quality samples efficiently. 3D-voxel-based
methods have recently shown great promise for generating high quality samples
de novo from random noise (Pinheiro et al., 2023). However, sampling in
3D-voxel space is computationally expensive and use in library generation is
prohibitively slow. Here, we instead perform neural empirical Bayes sampling
(Saremi & Hyvarinen, 2019) in the learned latent space of a vector-quantized
variational autoencoder. NEBULA generates large molecular libraries nearly an
order of magnitude faster than existing methods without sacrificing sample
quality. Moreover, NEBULA generalizes better to unseen drug-like molecules, as
demonstrated on two public datasets and multiple recently released drugs. We
expect the approach herein to be highly enabling for machine learning-based
drug discovery. The code is available at
https://github.com/prescient-design/nebula
