E(n) Equivariant Normalizing Flows for Molecule Generation in 3D
arXiv (Cornell University)(2021)
摘要
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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关键词
equivariant normalizing flows,molecule generation,3d
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