mlsanityData to 7 Oct 2026

Geometric Deep Learning on Molecular Representations

arXiv 2107.12375 ↗First posted 26 Jul 2021physics.chem-ph cs.AI cs.LGcited by 113

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Abstract (arXiv)

Geometric deep learning (GDL), which is based on neural network architectures that incorporate and process symmetry information, has emerged as a recent paradigm in artificial intelligence. GDL bears particular promise in molecular modeling applications, in which various molecular representations with different symmetry properties and levels of abstraction exist. This review provides a structured and harmonized overview of molecular GDL, highlighting its applications in drug discovery, chemical synthesis prediction, and quantum chemistry. Emphasis is placed on the relevance of the learned molecular features and their complementarity to well-established molecular descriptors. This review provides an overview of current challenges and opportunities, and presents a forecast of the future of GDL for molecular sciences.

Key facts. Cited by 113 papers in the map (data through 7 Oct 2026). Builds on 85 of its 209 references that are in the map. First posted on arXiv 26 Jul 2021.

Builds on

85 of 209 references.

References · 85

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38 more
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50 most cited of 85 All 85 (JSON)

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 2; 2022: 25; 2023: 29; 2024: 25; 2025: 20; 2026: 12.

113 papers.

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  1. 2022Structure-based Drug Design with Equivariant Diffusion Modelscited by 213
  2. 2022Graph neural networks for materials science and chemistrycited by 181
  3. 2022Multi-modal Molecule Structure-text Model for Text-based Retrieval and Editingcited by 165
  4. 2023FAENet: Frame Averaging Equivariant GNN for Materials Modelingcited by 72
  5. 2022ComENet: Towards Complete and Efficient Message Passing for 3D Molecular Graphscited by 69
  6. 2022Molecular Geometry Pretraining with SE(3)-Invariant Denoising Distance Matchingcited by 67
  7. 2021GeomGCL: Geometric Graph Contrastive Learning for Molecular Property Predictioncited by 61
  8. 2024Efficient Evolutionary Search Over Chemical Space with Large Language Modelscited by 59
  9. 2023A new perspective on building efficient and expressive 3D equivariant graph neural networkscited by 46
  10. 2022Structure-based drug design with geometric deep learningcited by 40
  11. 2024Enabling Efficient Equivariant Operations in the Fourier Basis via Gaunt Tensor Productscited by 35
  12. 2023A Survey on Graph Diffusion Models: Generative AI in Science for Molecule, Protein and Materialcited by 34
38 more
  1. 2022ACMP: Allen-Cahn Message Passing with Attractive and Repulsive Forces for Graph Neural Networkscited by 31
  2. 2023A graphon-signal analysis of graph neural networkscited by 29
  3. 2024A Survey of Geometric Graph Neural Networks: Data Structures, Models and Applicationscited by 28
  4. 2022Interpretable Geometric Deep Learning via Learnable Randomness Injectioncited by 27
  5. 2023Symmetry-Informed Geometric Representation for Molecules, Proteins, and Crystalline Materialscited by 22
  6. 2024LLaMo: Large Language Model-based Molecular Graph Assistantcited by 22
  7. 2022Graph Neural Networks for Moleculescited by 21
  8. 2022Controllable Data Generation by Deep Learning: A Reviewcited by 20
  9. 2024Beyond Euclid: An Illustrated Guide to Modern Machine Learning with Geometric, Topological, and Algebraic Structurescited by 20
  10. 2023Learning Long Sequences in Spiking Neural Networkscited by 19
  11. 2023A General Theory of Correct, Incorrect, and Extrinsic Equivariancecited by 18
  12. 2023Generalist Equivariant Transformer Towards 3D Molecular Interaction Learningcited by 18
  13. 2022Integration of Pre-trained Protein Language Models into Geometric Deep Learning Networkscited by 14
  14. 2023Molecular geometric deep learningcited by 13
  15. 2023Denoise Pretraining on Nonequilibrium Molecules for Accurate and Transferable Neural Potentialscited by 11
  16. 2024From Generalist to Specialist: A Survey of Large Language Models for Chemistrycited by 11
  17. 2022FlowGNN: A Dataflow Architecture for Real-Time Workload-Agnostic Graph Neural Network Inferencecited by 10
  18. 2023Identifying Semantic Component for Robust Molecular Property Predictioncited by 10
  19. 2023A Universal Framework for Accurate and Efficient Geometric Deep Learning of Molecular Systemscited by 10
  20. 2025Efficient and Scalable Density Functional Theory Hamiltonian Prediction through Adaptive Sparsitycited by 10
  21. 2025Generating 3D Binding Molecules Using Shape-Conditioned Diffusion Models with Guidancecited by 10
  22. 2022GenGNN: A Generic FPGA Framework for Graph Neural Network Accelerationcited by 9
  23. 2024Advancing Molecular Graph-Text Pre-training via Fine-grained Alignmentcited by 9
  24. 2023Learning Harmonic Molecular Representations on Riemannian Manifoldcited by 8
  25. 2023M2Hub: Unlocking the Potential of Machine Learning for Materials Discoverycited by 8
  26. 2022A Simple Yet Effective SVD-GCN for Directed Graphscited by 7
  27. 2023Molecular Hypergraph Neural Networkscited by 7
  28. 2023Autoregressive fragment-based diffusion for pocket-aware ligand designcited by 7
  29. 2022Robust Graph Representation Learning for Local Corruption Recoverycited by 6
  30. 2022Graph Machine Learning for Design of High-Octane Fuelscited by 6
  31. 2022Efficient and Accurate Physics-aware Multiplex Graph Neural Networks for 3D Small Molecules and Macromolecule Complexescited by 6
  32. 2022Approximate Equivariance SO(3) Needlet Convolutioncited by 6
  33. 2023Knowledge-augmented Graph Machine Learning for Drug Discovery: A Surveycited by 6
  34. 2023The Multiscale Surface Vision Transformercited by 6
  35. 2023Equivariance and partial observations in Koopman operator theory for partial differential equationscited by 6
  36. 2023A General Framework for Robust G-Invariance in G-Equivariant Networkscited by 5
  37. 2025DualEquiNet: A Dual-Space Hierarchical Equivariant Network for Large Biomoleculescited by 5
  38. 2025Molecular Machine Learning in Chemical Process Designcited by 5

50 most cited of 113 All 113 (JSON)