Deep generative models have been applied with increasing success to the
generation of two dimensional molecules as SMILES strings and molecular graphs.
In this work we describe for the first time a deep generative model that can
generate 3D molecular structures conditioned on a three-dimensional (3D)
binding pocket. Using convolutional neural networks, we encode atomic density
grids into separate receptor and ligand latent spaces. The ligand latent space
is variational to support sampling of new molecules. A decoder network
generates atomic densities of novel ligands conditioned on the receptor.
Discrete atoms are then fit to these continuous densities to create molecular
structures. We show that valid and unique molecules can be readily sampled from
the variational latent space defined by a reference `seed' structure and
generated structures have reasonable interactions with the binding site. As
structures are sampled farther in latent space from the seed structure, the
novelty of the generated structures increases, but the predicted binding
affinity decreases. Overall, we demonstrate the feasibility of conditional 3D
molecular structure generation and provide a starting point for methods that
also explicitly optimize for desired molecular properties, such as high binding
affinity.

本研究实现了基于深度生成模型、卷积神经网络和可变潜空间的三维分子结构生成，在此基础上，也为优化生成的分子性质提供了初始方法。