Accurate prediction of the three-dimensional structure of a molecule is critical to a wide range of cheminformatics and molecular modeling tasks, since electrostatic, intermolecular, and other conformation-driven properties depend on the interatomic distances. There is an increasing interest in the "inverse design" of molecules with optimal or near-optimal properties. For example, generative neural networks and genetic algorithms create molecules with desirable target properties. Moreover, many computational chemistry simulations, including molecular dynamics and quantum chemistry require full three-dimensional structures to run.