ArticleNature communications2024
Protein structure generation via folding diffusion.
Article in Nature communications, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 81 papers.
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Who cites it
81 citing papers in PubMed, 138 citations in OpenAlex.
- Artificial intelligence catalyzes antimicrobial peptide design.Synthetic and systems biotechnology · 2027Review
- DESIGN: a framework for generative AI-enabled crop molecular design-driven breeding.Science China. Life sciences · 2026Review
- Protein function-conditioned language models for variant effect prediction and controllable design.Biophysical journal · 2026Article
- DiTSim: A Diffusion-Transformers Based Single-Cell ATAC-seq Data Simulator.Interdisciplinary sciences, computational life sciences · 2026Article
- Attribution assignment for deep-generative sequence models enables interpretability analysis using positive-only data.PLoS computational biology · 2026Article
- Discovery and design of potent cell surface display elements.Nature biotechnology · 2026Article
- FoldDoF: Utilizing the Primary Degrees of Freedom of Protein Backbone for Geometric Modeling and Generation.Journal of chemical information and modeling · 2026Article
- AI-Physics-Experiment Trinity for Integrated Protein Dynamics Modeling.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Review
- Benchmarking Generative AI Protein Models Reveals Differences Between Structural and Sequence-based Approaches.Genomics, proteomics & bioinformatics · 2026Article
- Review
- Generalist biological artificial intelligence in modeling the language of life.Nature biotechnology · 2026Review
- In silico generation of gene expression profiles using diffusion models.BMC bioinformatics · 2026Article
- Protein foundation models: a comprehensive survey.Science China. Life sciences · 2026Review
- FunDiff: diffusion models over function spaces for physics-informed generative modeling.Nature communications · 2026Article
- IDPForge: Deep Learning of Proteins with Global and Local Regions of Disorder.bioRxiv : the preprint server for biology · 2026Article
- From machine learning to multimodal models: The AI revolution in enzyme engineering.Biodesign research · 2026Review
- Article
- Efficient Sampling of Short Protein Trajectories with Conditional Diffusion Models.Journal of chemical theory and computation · 2026Article
- Reinforced molecular dynamics: Physics-infused generative machine learning model simulates protein motion.PNAS nexus · 2026Article
- Accurate and Generalizable Protein-Ligand Binding Affinity Prediction With Geometric Deep Learning.IEEE open journal of engineering in medicine and biology · 2026Article
21 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors at 3 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The ability to computationally generate novel yet physically foldable protein structures could lead to new biological discoveries and new treatments targeting yet incurable diseases. Despite recent advances in protein structure prediction, directly generating diverse, novel protein structures from neural networks remains difficult. In this work, we present a diffusion-based generative model that generates protein backbone structures via a procedure inspired by the natural folding process. We describe a protein backbone structure as a sequence of angles capturing the relative orientation of the constituent backbone atoms, and generate structures by denoising from a random, unfolded state towards a stable folded structure. Not only does this mirror how proteins natively twist into energetically favorable conformations, the inherent shift and rotational invariance of this representation crucially alleviates the need for more complex equivariant networks. We train a denoising diffusion probabilistic model with a simple transformer backbone and demonstrate that our resulting model unconditionally generates highly realistic protein structures with complexity and structural patterns akin to those of naturally-occurring proteins. As a useful resource, we release an open-source codebase and trained models for protein structure diffusion.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.