ArticleScience advances2025
Learning the rules of peptide self-assembly through data mining with large language models.
Article in Science advances, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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The trial behind it
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Who cites it
9 citing papers in PubMed.
- Artificial intelligence-assisted design of self-assembling peptide hydrogels for neural regeneration: Principles and opportunities.Bioactive materials · 2027Review
- Combating antimicrobial resistance through multimodal data via artificial intelligence.One health (Amsterdam, Netherlands) · 2026Review
- Stimuli-responsive structural transformation of adaptive peptide nanomaterials toward biomedical applications.Materials today. Bio · 2026Review
- Predicting Supramolecular Self-Assembly of Peptide Structures with AlphaFold3.bioRxiv : the preprint server for biology · 2026Article
- Article
- Self-Assembling Short Peptide Carriers for Gene Delivery.International journal of molecular sciences · 2026Review
- Lipoengineering of Biomolecular Condensates Controls Material Properties and Multiphase Hierarchy to Guide Organoid Morphogenesis.bioRxiv : the preprint server for biology · 2026Article
- Deep generative modeling for AI-guided inverse design of perovskite photovoltaic devices.Frontiers in artificial intelligence · 2026Article
- Emerging self-assembled peptide hydrogels for enhanced wound healing.Nanomedicine (London, England) · 2025Review
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
Peptides are ubiquitous and important biomolecules that self-assemble into diverse structures. Although extensive research has explored the effects of chemical composition and exterior conditions on self-assembly, a systematic study consolidating these data to uncover global rules is lacking. In this work, we curate a peptide assembly database through a combination of manual processing by human experts and large language model-assisted literature mining. As a result, we collect over 1000 experimental data entries with information about peptide sequence, experimental conditions, and corresponding self-assembly phases. Using the data, machine learning models are developed, demonstrating excellent accuracy (>80%) in assembly phase classification. Moreover, we fine-tune a GPT model for peptide literature mining with the developed dataset, which markedly outperforms the pretrained model in extracting information from academic publications. This workflow can improve efficiency when exploring potential self-assembling peptide candidates, through guiding experimental work, while also deepening our understanding of the governing mechanisms.
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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.