Inverse design of 3d molecular structures with conditional generative neural networks
Abstract
The rational design of molecules with desired properties is a long-standing challenge in
chemistry. Generative neural networks have emerged as a powerful approach to sample
novel molecules from a learned distribution. Here, we propose a conditional generative neural
network for 3d molecular structures with specified chemical and structural properties. This
approach is agnostic to chemical bonding and enables targeted sampling of novel molecules
from conditional distributions, even in domains where reference calculations are sparse. We
demonstrate the utility of our method for inverse design by generating molecules with
specified motifs or composition, discovering particularly stable molecules, and jointly targeting multiple electronic properties beyond the training regime.
chemistry. Generative neural networks have emerged as a powerful approach to sample
novel molecules from a learned distribution. Here, we propose a conditional generative neural
network for 3d molecular structures with specified chemical and structural properties. This
approach is agnostic to chemical bonding and enables targeted sampling of novel molecules
from conditional distributions, even in domains where reference calculations are sparse. We
demonstrate the utility of our method for inverse design by generating molecules with
specified motifs or composition, discovering particularly stable molecules, and jointly targeting multiple electronic properties beyond the training regime.
