  We study a fundamental problem in computational chemistry known as molecular
conformation generation, trying to predict stable 3D structures from 2D
molecular graphs. Existing machine learning approaches usually first predict
distances between atoms and then generate a 3D structure satisfying the
distances, where noise in predicted distances may induce extra errors during 3D
coordinate generation. Inspired by the traditional force field methods for
molecular dynamics simulation, in this paper, we propose a novel approach
called ConfGF by directly estimating the gradient fields of the log density of
atomic coordinates. The estimated gradient fields allow directly generating
stable conformations via Langevin dynamics. However, the problem is very
challenging as the gradient fields are roto-translation equivariant. We notice
that estimating the gradient fields of atomic coordinates can be translated to
estimating the gradient fields of interatomic distances, and hence develop a
novel algorithm based on recent score-based generative models to effectively
estimate these gradients. Experimental results across multiple tasks show that
ConfGF outperforms previous state-of-the-art baselines by a significant margin.
