  Predicting molecular conformations (or 3D structures) from molecular graphs
is a fundamental problem in many applications. Most existing approaches are
usually divided into two steps by first predicting the distances between atoms
and then generating a 3D structure through optimizing a distance geometry
problem. However, the distances predicted with such two-stage approaches may
not be able to consistently preserve the geometry of local atomic
neighborhoods, making the generated structures unsatisfying. In this paper, we
propose an end-to-end solution for molecular conformation prediction called
ConfVAE based on the conditional variational autoencoder framework.
Specifically, the molecular graph is first encoded in a latent space, and then
the 3D structures are generated by solving a principled bilevel optimization
program. Extensive experiments on several benchmark data sets prove the
effectiveness of our proposed approach over existing state-of-the-art
approaches. Code is available at
\url{https://github.com/MinkaiXu/ConfVAE-ICML21}.
