mlsanityData to 14 Oct 2026

Towards equilibrium molecular conformation generation with GFlowNets

arXiv 2310.14782 ↗First posted 20 Oct 2023cs.LG cs.AIcited by 14

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Abstract (arXiv)

Sampling diverse, thermodynamically feasible molecular conformations plays a crucial role in predicting properties of a molecule. In this paper we propose to use GFlowNet for sampling conformations of small molecules from the Boltzmann distribution, as determined by the molecule's energy. The proposed approach can be used in combination with energy estimation methods of different fidelity and discovers a diverse set of low-energy conformations for highly flexible drug-like molecules. We demonstrate that GFlowNet can reproduce molecular potential energy surfaces by sampling proportionally to the Boltzmann distribution.

Key facts. Cited by 14 papers in the map (data through 14 Oct 2026). Builds on 17 of its 37 references that are in the map. First posted on arXiv 20 Oct 2023.

Builds on

17 of 37 references.

References · 17

  1. 2017Proximal Policy Optimization Algorithmscited by 15,215
  2. 2022GeoDiff: a Geometric Diffusion Model for Molecular Conformation Generationcited by 516
  3. 2021Flow Network based Generative Models for Non-Iterative Diverse Candidate Generationcited by 392
  4. 2022Torsional Diffusion for Molecular Conformer Generationcited by 263
  5. 2022Trajectory balance: Improved credit assignment in GFlowNetscited by 251
  6. 2022TorchMD-NET: Equivariant Transformers for Neural Network based Molecular Potentialscited by 182
  7. 2021Learning Gradient Fields for Molecular Conformation Generationcited by 163
  8. 2021GeoMol: Torsional Geometric Generation of Molecular 3D Conformer Ensemblescited by 107
  9. 2023A theory of continuous generative flow networkscited by 101
  10. 2022Forces are not Enough: Benchmark and Critical Evaluation for Machine Learning Force Fields with Molecular Simulationscited by 95
  11. 2021Learning Neural Generative Dynamics for Molecular Conformation Generationcited by 88
  12. 2023Two for One: Diffusion Models and Force Fields for Coarse-Grained Molecular Dynamicscited by 72
5 more
  1. 2018Machine Learning of coarse-grained Molecular Dynamics Force Fieldscited by 69
  2. 2021Smooth Normalizing Flowscited by 53
  3. 2023Towards Predicting Equilibrium Distributions for Molecular Systems with Deep Learningcited by 26
  4. 2020TorsionNet: A Reinforcement Learning Approach to Sequential Conformer Searchcited by 15
  5. 2023Von Mises Mixture Distributions for Molecular Conformation Generationcited by 5

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 7; 2025: 5; 2026: 2.

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  1. 2024RGFN: Synthesizable Molecular Generation Using GFlowNetscited by 41
  2. 2025From discrete-time policies to continuous-time diffusion samplers: Asymptotic equivalences and faster trainingcited by 19
  3. 2024RetroGFN: Diverse and Feasible Retrosynthesis using GFlowNetscited by 12
  4. 2025On scalable and efficient training of diffusion samplerscited by 10
  5. 2024MolBind: Multimodal Alignment of Language, Molecules, and Proteinscited by 7
  6. 2024MetaGFN: Exploring Distant Modes with Adapted Metadynamics for Continuous GFlowNetscited by 6
  7. 2025Adaptive Destruction Processes for Diffusion Samplerscited by 6
  8. 2024Towards DNA-Encoded Library Generation with GFlowNetscited by 5
  9. 2024Cell Morphology-Guided Small Molecule Generation with GFlowNetscited by 4
  10. 2025BSL: A Unified and Generalizable Multitask Learning Platform for Virtual Drug Discovery from Design to Synthesiscited by 1
  11. 2026AtomComposer: Discovering Chemical Space from First Principles with Reinforcement Learningcited by 1
  12. 2024Dynamic Backtracking in GFlowNets: Enhancing Decision Steps with Reward-Dependent Adjustment Mechanismscited by 0
2 more
  1. 2025Torsional-GFN: a conditional conformation generator for small moleculescited by 0
  2. 2026SPIBER: Reconstructing Free Energy Landscapes from Short, Unconverged Trajectories with Generative Flow Networkscited by 0