  We study a fundamental problem in structure-based drug design -- generating
molecules that bind to specific protein binding sites. While we have witnessed
the great success of deep generative models in drug design, the existing
methods are mostly string-based or graph-based. They are limited by the lack of
spatial information and thus unable to be applied to structure-based design
tasks. Particularly, such models have no or little knowledge of how molecules
interact with their target proteins exactly in 3D space. In this paper, we
propose a 3D generative model that generates molecules given a designated 3D
protein binding site. Specifically, given a binding site as the 3D context, our
model estimates the probability density of atom's occurrences in 3D space --
positions that are more likely to have atoms will be assigned higher
probability. To generate 3D molecules, we propose an auto-regressive sampling
scheme -- atoms are sampled sequentially from the learned distribution until
there is no room for new atoms. Combined with this sampling scheme, our model
can generate valid and diverse molecules, which could be applicable to various
structure-based molecular design tasks such as molecule sampling and linker
design. Experimental results demonstrate that molecules sampled from our model
exhibit high binding affinity to specific targets and good drug properties such
as drug-likeness even if the model is not explicitly optimized for them.
