mlsanityData to 14 Oct 2026

Towards Predicting Equilibrium Distributions for Molecular Systems with Deep Learning

arXiv 2306.05445 ↗First posted 8 Jun 2023physics.chem-ph cs.LG q-bio.BMcited by 26

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Abstract (arXiv)

Advances in deep learning have greatly improved structure prediction of molecules. However, many macroscopic observations that are important for real-world applications are not functions of a single molecular structure, but rather determined from the equilibrium distribution of structures. Traditional methods for obtaining these distributions, such as molecular dynamics simulation, are computationally expensive and often intractable. In this paper, we introduce a novel deep learning framework, called Distributional Graphormer (DiG), in an attempt to predict the equilibrium distribution of molecular systems. Inspired by the annealing process in thermodynamics, DiG employs deep neural networks to transform a simple distribution towards the equilibrium distribution, conditioned on a descriptor of a molecular system, such as a chemical graph or a protein sequence. This framework enables efficient generation of diverse conformations and provides estimations of state densities. We demonstrate the performance of DiG on several molecular tasks, including protein conformation sampling, ligand structure sampling, catalyst-adsorbate sampling, and property-guided structure generation. DiG presents a significant advancement in methodology for statistically understanding molecular systems, opening up new research opportunities in molecular science.

Key facts. Cited by 26 papers in the map (data through 14 Oct 2026). Builds on 34 of its 102 references that are in the map. First posted on arXiv 8 Jun 2023.

Builds on

34 of 102 references.

References · 34

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22 more
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Citing papers per year: 2023: 12; 2024: 13; 2025: 1.

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  2. 2023The Impact of Large Language Models on Scientific Discovery: a Preliminary Study using GPT-4cited by 112
  3. 2024AlphaFold Meets Flow Matching for Generating Protein Ensemblescited by 110
  4. 2023MatterGen: a generative model for inorganic materials designcited by 98
  5. 2023Towards Foundation Models for Knowledge Graph Reasoningcited by 86
  6. 2024Position: Graph Foundation Models are Already Herecited by 86
  7. 2024GFT: Graph Foundation Model with Transferable Tree Vocabularycited by 85
  8. 2023A Hitchhiker's Guide to Geometric GNNs for 3D Atomic Systemscited by 68
  9. 2024Generative Modeling of Molecular Dynamics Trajectoriescited by 51
  10. 2024Protein Conformation Generation via Force-Guided SE(3) Diffusion Modelscited by 45
  11. 2024Space Group Informed Transformer for Crystalline Materials Generationcited by 41
  12. 2023From Molecules to Materials: Pre-training Large Generalizable Models for Atomic Property Predictioncited by 39
14 more
  1. 2024Understanding and Improving Training-free Loss-based Diffusion Guidancecited by 32
  2. 2023Crystal-GFN: sampling crystals with desirable properties and constraintscited by 31
  3. 2024Structure Language Models for Protein Conformation Generationcited by 17
  4. 2023Score dynamics: scaling molecular dynamics with picoseconds timestep via conditional diffusion modelcited by 16
  5. 2023Towards equilibrium molecular conformation generation with GFlowNetscited by 14
  6. 2023Overcoming the Barrier of Orbital-Free Density Functional Theory for Molecular Systems Using Deep Learningcited by 13
  7. 2024Harmonic Path Integral Diffusioncited by 11
  8. 2023Spectroscopy-Guided Discovery of Three-Dimensional Structures of Disordered Materials with Diffusion Modelscited by 8
  9. 2024AdsorbDiff: Adsorbate Placement via Conditional Denoising Diffusioncited by 8
  10. 2024Energy based diffusion generator for efficient sampling of Boltzmann distributionscited by 7
  11. 2024Generative Model for Constructing Reaction Path from Initial to Final Statescited by 1
  12. 2024Fusing Neural and Physical: Augment Protein Conformation Sampling with Tractable Simulationscited by 1
  13. 2025Adaptive Path Integral Diffusion: AdaPIDcited by 1
  14. 2023Mixup-Augmented Meta-Learning for Sample-Efficient Fine-Tuning of Protein Simulatorscited by 0