mlsanityData to 7 Oct 2026

3D Molecular Geometry Analysis with 2D Graphs

arXiv 2305.13315 ↗First posted 1 May 2023physics.chem-ph cs.AI cs.LGcited by 1

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Abstract (arXiv)

Ground-state 3D geometries of molecules are essential for many molecular analysis tasks. Modern quantum mechanical methods can compute accurate 3D geometries but are computationally prohibitive. Currently, an efficient alternative to computing ground-state 3D molecular geometries from 2D graphs is lacking. Here, we propose a novel deep learning framework to predict 3D geometries from molecular graphs. To this end, we develop an equilibrium message passing neural network (EMPNN) to better capture ground-state geometries from molecular graphs. To provide a testbed for 3D molecular geometry analysis, we develop a benchmark that includes a large-scale molecular geometry dataset, data splits, and evaluation protocols. Experimental results show that EMPNN can efficiently predict more accurate ground-state 3D geometries than RDKit and other deep learning methods. Results also show that the proposed framework outperforms self-supervised learning methods on property prediction tasks.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 25 of its 50 references that are in the map. First posted on arXiv 1 May 2023.

Builds on

25 of 50 references.

References · 25

  1. 2017Neural Message Passing for Quantum Chemistrycited by 3,460
  2. 2020Open Graph Benchmark: Datasets for Machine Learning on Graphscited by 2,148
  3. 2020Graph Contrastive Learning with Augmentationscited by 1,203
  4. 2017MoleculeNet: A Benchmark for Molecular Machine Learningcited by 1,037
  5. 2020SE(3)-Transformers: 3D Roto-Translation Equivariant Attention Networkscited by 602
  6. 2017SchNet: A continuous-filter convolutional neural network for modeling quantum interactionscited by 569
  7. 2020Directional Message Passing for Molecular Graphscited by 556
  8. 2019Analyzing Learned Molecular Representations for Property Predictioncited by 373
  9. 2021GemNet: Universal Directional Graph Neural Networks for Moleculescited by 342
  10. 2021OGB-LSC: A Large-Scale Challenge for Machine Learning on Graphscited by 328
  11. 2020Towards Deeper Graph Neural Networkscited by 296
  12. 2021Graph Contrastive Learning Automatedcited by 287
13 more
  1. 2020Fast and Uncertainty-Aware Directional Message Passing for Non-Equilibrium Moleculescited by 254
  2. 2021Pre-training Molecular Graph Representation with 3D Geometrycited by 248
  3. 2020GEOM: Energy-annotated molecular conformations for property prediction and molecular generationcited by 238
  4. 2021Learning Gradient Fields for Molecular Conformation Generationcited by 162
  5. 2021GeoMol: Torsional Geometric Generation of Molecular 3D Conformer Ensemblescited by 106
  6. 2019Molecular geometry prediction using a deep generative graph neural networkcited by 91
  7. 2021Learning Neural Generative Dynamics for Molecular Conformation Generationcited by 87
  8. 2019A Generative Model for Molecular Distance Geometrycited by 73
  9. 2020ATOM3D: Tasks On Molecules in Three Dimensionscited by 73
  10. 2021An End-to-End Framework for Molecular Conformation Generation via Bilevel Programmingcited by 59
  11. 2020Advanced Graph and Sequence Neural Networks for Molecular Property Prediction and Drug Discoverycited by 46
  12. 2021Directional Message Passing on Molecular Graphs via Synthetic Coordinatescited by 24
  13. 2021Fast Quantum Property Prediction via Deeper 2D and 3D Graph Networkscited by 10

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Citing papers per year: 2024: 1.

1 paper.

  1. 2024MolBind: Multimodal Alignment of Language, Molecules, and Proteinscited by 7