GraphEBM: Molecular Graph Generation with Energy-Based Models
- URL: http://arxiv.org/abs/2102.00546v1
- Date: Sun, 31 Jan 2021 21:45:12 GMT
- Title: GraphEBM: Molecular Graph Generation with Energy-Based Models
- Authors: Meng Liu, Keqiang Yan, Bora Oztekin, Shuiwang Ji
- Abstract summary: We propose GraphEBM to generate molecular graphs using energy-based models.
We parameterize the energy function in a permutation invariant manner, thus making GraphEBM permutation invariant.
To generate molecules with a specific desirable property, we propose a simple yet effective strategy, which pushes down energies with flexible degrees according to the properties of corresponding molecules.
- Score: 42.24111543958905
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Molecular graph generation is an emerging area of research with numerous
applications. This problem remains challenging as molecular graphs are
discrete, irregular, and permutation invariant to node order. Notably, most
existing approaches fail to guarantee the intrinsic property of permutation
invariance, resulting in unexpected bias in generative models. In this work, we
propose GraphEBM to generate molecular graphs using energy-based models. In
particular, we parameterize the energy function in a permutation invariant
manner, thus making GraphEBM permutation invariant. We apply Langevin dynamics
to train the energy function by approximately maximizing likelihood and
generate samples with low energies. Furthermore, to generate molecules with a
specific desirable property, we propose a simple yet effective strategy, which
pushes down energies with flexible degrees according to the properties of
corresponding molecules. Finally, we explore the use of GraphEBM for generating
molecules with multiple objectives in a compositional manner. Comprehensive
experimental results on random, goal-directed, and compositional generation
tasks demonstrate the effectiveness of our proposed method.
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