CUED Publications database

A Generative Model for Molecular Distance Geometry

Simm, GNC and Hernández-Lobato, JM A Generative Model for Molecular Distance Geometry. (Unpublished)

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Great computational effort is invested in generating equilibrium states for molecular systems using, for example, Markov chain Monte Carlo. We present a probabilistic model that generates statistically independent 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.

Item Type: Article
Uncontrolled Keywords: stat.ML stat.ML cs.LG
Divisions: Div F > Computational and Biological Learning
Depositing User: Cron Job
Date Deposited: 25 Oct 2019 21:08
Last Modified: 27 Aug 2020 04:27