  Sampling diverse, thermodynamically feasible molecular conformations plays a
crucial role in predicting properties of a molecule. In this paper we propose
to use GFlowNet for sampling conformations of small molecules from the
Boltzmann distribution, as determined by the molecule's energy. The proposed
approach can be used in combination with energy estimation methods of different
fidelity and discovers a diverse set of low-energy conformations for highly
flexible drug-like molecules. We demonstrate that GFlowNet can reproduce
molecular potential energy surfaces by sampling proportionally to the Boltzmann
distribution.
