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Poster
A Generative Model for Molecular Distance Geometry
Gregor Simm · Jose Miguel Hernandez-Lobato

Thu Jul 16 01:00 PM -- 01:45 PM & Fri Jul 17 02:00 AM -- 02:45 AM (PDT) @
in Poster Session 46 »

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.

Keywords: Computational Biology and Genomics · Deep Generative Models · Representation Learning · Autoencoders · Applications - Neuroscience, Cognitive Science, Biology and Health

Author Information

Gregor Simm (University of Cambridge)
Jose Miguel Hernandez-Lobato (University of Cambridge)

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