ArticleNature communications2025
Cyclic peptide structure prediction and design using AlphaFold2.
Article in Nature communications, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 73 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
73 citing papers in PubMed.
- Benchmarking AI-driven protocols for cyclic peptide conformer prediction and ranking.RSC medicinal chemistry · 2026Article
- A scalable recombinant pipeline for disulphide-stapled peptides.bioRxiv : the preprint server for biology · 2026Article
- Anticancer Peptides: Design Principles, Translational Bottlenecks, and Emerging Opportunities.Molecules (Basel, Switzerland) · 2026Review
- Peptide Aptamers: Innovative Design and Applications in Pathogen Detection.Chembiochem : a European journal of chemical biology · 2026Review
- HighFold4: extending AlphaFold3 to accurate cyclic peptide conformation prediction via custom chemical connectivity.Briefings in bioinformatics · 2026Article
- HFGuidedDesign:Chemical science · 2026Article
- Cyclic Peptides in Modern Drug Discovery: Trends and Therapeutic Directions.Journal of medicinal chemistry · 2026Review
- Structural Diversity and Differential Natural Pairings of MAT1-1-1 and MAT1-2-1 Proteins Essential for Sexual Reproduction inInternational journal of molecular sciences · 2026Article
- HighDB: a structure-annotated cyclic peptide database for comparative analysis, template retrieval, and design-oriented applications.Journal of computer-aided molecular design · 2026Article
- Cyclic Peptides as Modulators of Protein-Protein Interactions: A Survival Guide from Discovery Platforms to AI-Driven Design.International journal of molecular sciences · 2026Review
- Artificial intelligence in biologic drug discovery: A review of methodological evolution and therapeutic applications.Acta pharmaceutica Sinica. B · 2026Review
- Targeting the Undruggable: Deep Learning-Driven Design of Peptide Therapeutics in Cancer.Pharmaceuticals (Basel, Switzerland) · 2026Review
- Protein design and RNA design: Perspectives.Quantitative biology (Beijing, China) · 2026Article
- Mechanistic Dissection of Conformational Transition of Bicyclic Peptide via Molecular Modeling and Deep Learning.bioRxiv : the preprint server for biology · 2026Article
- Scaffold-Lab: Critical evaluation and ranking of protein backbone generation methods in a unified framework.PLoS computational biology · 2026Article
- A Versatile Strategy for Head-to-Tail Macrocyclization and Traceless Backbone Editing of Short Peptides.Journal of the American Chemical Society · 2026Article
- Tropical psychotria plants are a rich source for peptide inhibitors of human prolyl oligopeptidase.Natural products and bioprospecting · 2026Article
- Review
- Fast Generation of Simulation-Quality Structural Ensembles of Mixed-Chirality Cyclic Peptides via Diffusion Models.Journal of chemical theory and computation · 2026Article
- Stepwise transcription stalling by the anti-cancer drug Actinomycin D and insights into short tandem repeat transcription inhibition.Nature communications · 2026Article
13 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
17 authors.
Funding
Abstract
Small cyclic peptides have gained significant traction as a therapeutic modality; however, the development of deep learning methods for accurately designing such peptides has been slow, mostly due to the lack of sufficiently large training sets. Here, we introduce AfCycDesign, a deep learning approach for accurate structure prediction, sequence redesign, and de novo hallucination of cyclic peptides. Using AfCycDesign, we identified over 10,000 structurally-diverse designs predicted to fold into the designed structures with high confidence. X-ray crystal structures for eight tested de novo designed sequences match very closely with the design models (RMSD < 1.0 Å), highlighting the atomic level accuracy in our approach. Further, we used the set of hallucinated peptides as starting scaffolds to design binders with nanomolar IC
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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.