@incollection{icml2020_3679,
abstract = {Computing equilibrium states for many-body systems, such as molecules, is a long-standing challenge. In the absence of methods for generating statistically independent samples, great computational effort is invested in simulating these systems using, for example, Markov chain Monte Carlo. We present a probabilistic model that generates such samples for molecules from their graph representations.
Our model learns a low-dimensional manifold that preserves the geometry of local atomic neighborhoods through a principled learning representation that is based on Euclidean distance geometry. In a new benchmark for molecular conformation generation, we show experimentally that our generative model achieves state-of-the-art accuracy. Finally, we show how to use our model as a proposal distribution in an importance sampling scheme to compute molecular properties.},
author = {Simm, Gregor and Hernandez-Lobato, Jose Miguel},
booktitle = {Proceedings of Machine Learning and Systems 2020},
pages = {6642--6651},
title = {A Generative Model for Molecular Distance Geometry},
year = {2020}
}