mlsanityData to 7 Oct 2026

From thermodynamics to protein design: Diffusion models for biomolecule generation towards autonomous protein engineering

arXiv 2501.02680 ↗First posted 5 Jan 2025q-bio.QM cs.AI cs.LGcited by 2

On the map: Diffusion models → Flow matching, time series and science

Abstract (arXiv)

Protein design with desirable properties has been a significant challenge for many decades. Generative artificial intelligence is a promising approach and has achieved great success in various protein generation tasks. Notably, diffusion models stand out for their robust mathematical foundations and impressive generative capabilities, offering unique advantages in certain applications such as protein design. In this review, we first give the definition and characteristics of diffusion models and then focus on two strategies: Denoising Diffusion Probabilistic Models and Score-based Generative Models, where DDPM is the discrete form of SGM. Furthermore, we discuss their applications in protein design, peptide generation, drug discovery, and protein-ligand interaction. Finally, we outline the future perspectives of diffusion models to advance autonomous protein design and engineering. The E(3) group consists of all rotations, reflections, and translations in three-dimensions. The equivariance on the E(3) group can keep the physical stability of the frame of each amino acid as much as possible, and we reflect on how to keep the diffusion model E(3) equivariant for protein generation.

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Placed in Flow matching, time series and science, one of 14 directions in Diffusion models. Builds on 8 of its 120 references that are in the map. First posted on arXiv 5 Jan 2025.

Builds on

8 of 120 references.

Closest ancestors · 3

Same direction, newest first

  1. 2024Sifting through the Noise: A Survey of Diffusion Probabilistic Models and Their Applications to Biomolecules31 May
  2. 2023Navigating the Design Space of Equivariant Diffusion-Based Generative Models for De Novo 3D Molecule Generation29 Sep
  3. 2023DiffDock-PP: Rigid Protein-Protein Docking with Diffusion Models8 Apr

Other references · 5

  1. 2020Score-Based Generative Modeling through Stochastic Differential Equationscited by 8,964
  2. 2021Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gaugescited by 1,131
  3. 2024DPLM-2: A Multimodal Diffusion Protein Language Modelcited by 59
  4. 2022Symmetry Group Equivariant Architectures for Physicscited by 23
  5. 2024VN-EGNN: E(3)-Equivariant Graph Neural Networks with Virtual Nodes Enhance Protein Binding Site Identificationcited by 14

Built on by

Cited by year

Citing papers per year: 2025: 2.

2 papers.

  1. 2025The Effect of Stochasticity in Score-Based Diffusion Sampling: a KL Divergence Analysiscited by 1
  2. 2025Diffusion Models at the Drug Discovery Frontier: A Review on Generating Small Molecules versus Therapeutic Peptidescited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2025The Dance of Atoms-De Novo Protein Design with Diffusion Model
  2. 2026Generative Modeling in Protein Design: Neural Representations, Conditional Generation, and Evaluation Standards
  3. 2022Protein Structure and Sequence Generation with Equivariant Denoising Diffusion Probabilistic Models
  4. 2025Demystify Protein Generation with Hierarchical Conditional Diffusion Models
  5. 2023A Latent Diffusion Model for Protein Structure Generation
  6. 2024Diffusion Models in textitDe Novo Drug Design
  7. 2022DiffBP: Generative Diffusion of 3D Molecules for Target Protein Binding
  8. 2023Generating Novel, Designable, and Diverse Protein Structures by Equivariantly Diffusing Oriented Residue Clouds