  Learning representations of molecular structures using deep learning is a
fundamental problem in molecular property prediction tasks. Molecules
inherently exist in the real world as three-dimensional structures;
furthermore, they are not static but in continuous motion in the 3D Euclidean
space, forming a potential energy surface. Therefore, it is desirable to
generate multiple conformations in advance and extract molecular
representations using a 4D-QSAR model that incorporates multiple conformations.
However, this approach is impractical for drug and material discovery tasks
because of the computational cost of obtaining multiple conformations. To
address this issue, we propose a pre-training method for molecular GNNs using
an existing dataset of molecular conformations to generate a latent vector
universal to multiple conformations from a 2D molecular graph. Our method,
called Boltzmann GNN, is formulated by maximizing the conditional marginal
likelihood of a conditional generative model for conformations generation. We
show that our model has a better prediction performance for molecular
properties than existing pre-training methods using molecular graphs and
three-dimensional molecular structures.
