A Generative Model for Molecular Distance Geometry

Gregor N. C. Simm; José Miguel Hernández-Lobato

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arXiv · 1909.11459

A Generative Model for Molecular Distance Geometry

Gregor N. C. Simm·José Miguel Hernández-Lobato

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Abstract

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.

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Gregor N. C. Simm, José Miguel Hernández-Lobato. 2019-09-25. A Generative Model for Molecular Distance Geometry. https://arxiv.org/abs/1909.11459

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