The goal of structure-based drug discovery is to find small molecules that
bind to a given target protein. Deep learning has been used to generate
drug-like molecules with certain cheminformatic properties, but has not yet
been applied to generating 3D molecules predicted to bind to proteins by
sampling the conditional distribution of protein-ligand binding interactions.
In this work, we describe for the first time a deep learning system for
generating 3D molecular structures conditioned on a receptor binding site. We
approach the problem using a conditional variational autoencoder trained on an
atomic density grid representation of cross-docked protein-ligand structures.
We apply atom fitting and bond inference procedures to construct valid
molecular conformations from generated atomic densities. We evaluate the
properties of the generated molecules and demonstrate that they change
significantly when conditioned on mutated receptors. We also explore the latent
space learned by our generative model using sampling and interpolation
techniques. This work opens the door for end-to-end prediction of stable
bioactive molecules from protein structures with deep learning.

本文提出了一种新的基于深度学习的方法来生成可预测结合蛋白质的三维分子结构，对生成的分子进行了性质评估，并探索了生成模型的潜在空间，为利用深度学习从蛋白结构预测稳定的生物活性分子开启了新的可能性。