E(n) Equivariant Normalizing Flows for Molecule Generation in 3D

• URL: http://arxiv.org/abs/2105.09016v1
• Date: Wed, 19 May 2021 09:28:54 GMT
• Title: E(n) Equivariant Normalizing Flows for Molecule Generation in 3D
• Authors: Victor Garcia Satorras, Emiel Hoogeboom, Fabian B. Fuchs, Ingmar Posner, Max Welling
• Abstract summary: This paper introduces a generative model equivariant to Euclidean symmetries: E(n) Equivariant Normalizing Flows (E-NFs) To the best of our knowledge, this is the first likelihood-based deep generative model that generates molecules in 3D.
• Score: 87.12477361140716
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This paper introduces a generative model equivariant to Euclidean symmetries: E(n) Equivariant Normalizing Flows (E-NFs). To construct E-NFs, we take the discriminative E(n) graph neural networks and integrate them as a differential equation to obtain an invertible equivariant function: a continuous-time normalizing flow. We demonstrate that E-NFs considerably outperform baselines and existing methods from the literature on particle systems such as DW4 and LJ13, and on molecules from QM9 in terms of log-likelihood. To the best of our knowledge, this is the first likelihood-based deep generative model that generates molecules in 3D.

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