mlsanityData to 7 Oct 2026

Generating valid Euclidean distance matrices

arXiv 1910.03131 ↗First posted 7 Oct 2019cs.LG stat.MLcited by 19

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Abstract (arXiv)

Generating point clouds, e.g., molecular structures, in arbitrary rotations, translations, and enumerations remains a challenging task. Meanwhile, neural networks utilizing symmetry invariant layers have been shown to be able to optimize their training objective in a data-efficient way. In this spirit, we present an architecture which allows to produce valid Euclidean distance matrices, which by construction are already invariant under rotation and translation of the described object. Motivated by the goal to generate molecular structures in Cartesian space, we use this architecture to construct a Wasserstein GAN utilizing a permutation invariant critic network. This makes it possible to generate molecular structures in a one-shot fashion by producing Euclidean distance matrices which have a three-dimensional embedding.

Key facts. Cited by 19 papers in the map (data through 7 Oct 2026). First posted on arXiv 7 Oct 2019.

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Citing papers per year: 2021: 5; 2022: 6; 2023: 4; 2024: 4.

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  1. 2022Equivariant Diffusion for Molecule Generation in 3Dcited by 666
  2. 2022Generating 3D Molecules for Target Protein Bindingcited by 104
  3. 2021Inverse design of 3d molecular structures with conditional generative neural networkscited by 93
  4. 2021Learning Neural Generative Dynamics for Molecular Conformation Generationcited by 87
  5. 2022Equivariant Energy-Guided SDE for Inverse Molecular Designcited by 72
  6. 2021An End-to-End Framework for Molecular Conformation Generation via Bilevel Programmingcited by 59
  7. 2022Direct Molecular Conformation Generationcited by 30
  8. 2023An Equivariant Generative Framework for Molecular Graph-Structure Co-Designcited by 10
  9. 2024Geometry Informed Tokenization of Molecules for Language Model Generationcited by 10
  10. 2022Accurate Machine Learned Quantum-Mechanical Force Fields for Biomolecular Simulationscited by 9
  11. 2023SILVR: Guided Diffusion for Molecule Generationcited by 9
  12. 2021Auto-Encoding Molecular Conformationscited by 8
7 more
  1. 2021Augmenting Molecular Deep Generative Models with Topological Data Analysis Representationscited by 6
  2. 2024Generating Physical Dynamics under Priorscited by 6
  3. 2024Text-guided Diffusion Model for 3D Molecule Generationcited by 5
  4. 2023Geometric Constraints in Probabilistic Manifolds: A Bridge from Molecular Dynamics to Structured Diffusion Processescited by 1
  5. 2024On Diffusion Process in SE(3)-invariant Spacecited by 1
  6. 2022Cluster Generation via Deep Energy-Based Modelcited by 0
  7. 2023On Accelerating Diffusion-based Molecular Conformation Generation in SE(3)-invariant Spacecited by 0