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Found 4 papers, 3 papers with code
GraphEBM: Towards Permutation Invariant and Multi-Objective Molecular Graph Generation
In this work, we propose GraphEBM, a molecular graph generation method via energy-based models (EBMs), as an exploratory work to perform permutation invariant and multi-objective molecule generation.
DIG: A Turnkey Library for Diving into Graph Deep Learning Research
Although there exist several libraries for deep learning on graphs, they are aiming at implementing basic operations for graph deep learning.
Spherical Message Passing for 3D Graph Networks
Based on such observations, we propose the spherical message passing (SMP) as a novel and powerful scheme for 3D molecular learning.
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GraphEBM: Molecular Graph Generation with Energy-Based Models
We note that most existing approaches for molecular graph generation fail to guarantee the intrinsic property of permutation invariance, resulting in unexpected bias in generative models.
