mlsanityData to 7 Oct 2026

Direct Molecular Conformation Generation

arXiv 2202.01356 ↗First posted 3 Feb 2022cs.AIcited by 30

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Abstract (arXiv)

Molecular conformation generation aims to generate three-dimensional coordinates of all the atoms in a molecule and is an important task in bioinformatics and pharmacology. Previous methods usually first predict the interatomic distances, the gradients of interatomic distances or the local structures (e.g., torsion angles) of a molecule, and then reconstruct its 3D conformation. How to directly generate the conformation without the above intermediate values is not fully explored. In this work, we propose a method that directly predicts the coordinates of atoms: (1) the loss function is invariant to roto-translation of coordinates and permutation of symmetric atoms; (2) the newly proposed model adaptively aggregates the bond and atom information and iteratively refines the coordinates of the generated conformation. Our method achieves the best results on GEOM-QM9 and GEOM-Drugs datasets. Further analysis shows that our generated conformations have closer properties (e.g., HOMO-LUMO gap) with the groundtruth conformations. In addition, our method improves molecular docking by providing better initial conformations. All the results demonstrate the effectiveness of our method and the great potential of the direct approach. The code is released at https://github.com/DirectMolecularConfGen/DMCG

Key facts. Cited by 30 papers in the map (data through 7 Oct 2026). Builds on 21 of its 54 references that are in the map. First posted on arXiv 3 Feb 2022.

Builds on

21 of 54 references.

References · 21

  1. 2017Attention Is All You Needcited by 59,932
  2. 2014Neural Machine Translation by Jointly Learning to Align and Translatecited by 4,530
  3. 2018Relational inductive biases, deep learning, and graph networkscited by 1,341
  4. 2021How Attentive are Graph Attention Networks?cited by 818
  5. 2021OGB-LSC: A Large-Scale Challenge for Machine Learning on Graphscited by 328
  6. 2020GraphAF: a Flow-based Autoregressive Model for Molecular Graph Generationcited by 301
  7. 2020GEOM: Energy-annotated molecular conformations for property prediction and molecular generationcited by 238
  8. 2017Photographic Image Synthesis with Cascaded Refinement Networkscited by 223
  9. 2019Diagnosing and Enhancing VAE Modelscited by 192
  10. 2019Symmetry-adapted generation of 3d point sets for the targeted discovery of moleculescited by 171
  11. 2021Learning Gradient Fields for Molecular Conformation Generationcited by 162
  12. 2021Scalars are universal: Equivariant machine learning, structured like classical physicscited by 127
9 more
  1. 2021GeoMol: Torsional Geometric Generation of Molecular 3D Conformer Ensemblescited by 106
  2. 2019Molecular geometry prediction using a deep generative graph neural networkcited by 91
  3. 2021Learning Neural Generative Dynamics for Molecular Conformation Generationcited by 87
  4. 2021SE(3) Equivariant Graph Neural Networks with Complete Local Framescited by 75
  5. 2019A Generative Model for Molecular Distance Geometrycited by 73
  6. 2021An End-to-End Framework for Molecular Conformation Generation via Bilevel Programmingcited by 59
  7. 2019Generating valid Euclidean distance matricescited by 19
  8. 2021Large-scale graph representation learning with very deep GNNs and self-supervisioncited by 18
  9. 2021Auto-Encoding Molecular Conformationscited by 8

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Cited by year

Citing papers per year: 2022: 4; 2023: 12; 2024: 9; 2025: 5.

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  1. 2022GeoDiff: a Geometric Diffusion Model for Molecular Conformation Generationcited by 511
  2. 2022MolGenSurvey: A Systematic Survey in Machine Learning Models for Molecule Designcited by 66
  3. 2022Unified 2D and 3D Pre-Training of Molecular Representationscited by 45
  4. 2023A Group Symmetric Stochastic Differential Equation Model for Molecule Multi-modal Pretrainingcited by 32
  5. 2022Protein Sequence and Structure Co-Design with Equivariant Translationcited by 31
  6. 2024A Survey of Geometric Graph Neural Networks: Data Structures, Models and Applicationscited by 28
  7. 2023DiffSDS: A language diffusion model for protein backbone inpainting under geometric conditions and constraintscited by 10
  8. 2023Deep Learning Methods for Small Molecule Drug Discovery: A Surveycited by 10
  9. 2024Weisfeiler Leman for Euclidean Equivariant Machine Learningcited by 10
  10. 2023Do Deep Learning Methods Really Perform Better in Molecular Conformation Generation?cited by 8
  11. 2024FABind+: Enhancing Molecular Docking through Improved Pocket Prediction and Pose Generationcited by 8
  12. 2023PrefixMol: Target- and Chemistry-aware Molecule Design via Prefix Embeddingcited by 7
18 more
  1. 2023CoarsenConf: Equivariant Coarsening with Aggregated Attention for Molecular Conformer Generationcited by 7
  2. 2024Deep Geometry Handling and Fragment-wise Molecular 3D Graph Generationcited by 5
  3. 2023GraphVF: Controllable Protein-Specific 3D Molecule Generation with Variational Flowcited by 4
  4. 2023Gradual Optimization Learning for Conformational Energy Minimizationcited by 4
  5. 2025Efficient Molecular Conformer Generation with SO(3)-Averaged Flow Matching and Reflowcited by 4
  6. 2023Accurate and Efficient Structural Ensemble Generation of Macrocyclic Peptides using Internal Coordinate Diffusioncited by 3
  7. 2024Physical Consistency Bridges Heterogeneous Data in Molecular Multi-Task Learningcited by 3
  8. 2023Molecular Conformation Generation via Shifting Scorescited by 2
  9. 2024Diffusion-based Graph Generative Methodscited by 2
  10. 2024Equivariant Blurring Diffusion for Hierarchical Molecular Conformer Generationcited by 2
  11. 2025Beyond Atoms: Enhancing Molecular Pretrained Representations with 3D Space Modelingcited by 2
  12. 2023Infinite Physical Monkey: Do Deep Learning Methods Really Perform Better in Conformation Generation?cited by 1
  13. 2024On Diffusion Process in SE(3)-invariant Spacecited by 1
  14. 2024Riemannian Denoising Model for Molecular Structure Optimization with Chemical Accuracycited by 1
  15. 2023On Accelerating Diffusion-based Molecular Conformation Generation in SE(3)-invariant Spacecited by 0
  16. 2025A standard transformer and attention with linear biases for molecular conformer generationcited by 0
  17. 2025Diffusion Models at the Drug Discovery Frontier: A Review on Generating Small Molecules versus Therapeutic Peptidescited by 0
  18. 2025Chemistry-Enhanced Diffusion-Based Framework for Small-to-Large Molecular Conformation Generationcited by 0