  A molecule's geometry, also known as conformation, is one of a molecule's
most important properties, determining the reactions it participates in, the
bonds it forms, and the interactions it has with other molecules. Conventional
conformation generation methods minimize hand-designed molecular force field
energy functions that are often not well correlated with the true energy
function of a molecule observed in nature. They generate geometrically diverse
sets of conformations, some of which are very similar to the lowest-energy
conformations and others of which are very different. In this paper, we propose
a conditional deep generative graph neural network that learns an energy
function by directly learning to generate molecular conformations that are
energetically favorable and more likely to be observed experimentally in
data-driven manner. On three large-scale datasets containing small molecules,
we show that our method generates a set of conformations that on average is far
more likely to be close to the corresponding reference conformations than are
those obtained from conventional force field methods. Our method maintains
geometrical diversity by generating conformations that are not too similar to
each other, and is also computationally faster. We also show that our method
can be used to provide initial coordinates for conventional force field
methods. On one of the evaluated datasets we show that this combination allows
us to combine the best of both methods, yielding generated conformations that
are on average close to reference conformations with some very similar to
reference conformations.
