ReviewRSC chemical biology2024
Leveraging machine learning models for peptide-protein interaction prediction.
Review in RSC chemical biology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
28 citing papers in PubMed.
- PEARL: novel protein targets recommendation model via heterogeneous graph representation learning.Briefings in bioinformatics · 2026Article
- Resnet-Driven In Silico Identification of Lead Peptides from the Venom Gland Transcriptome ofPharmaceuticals (Basel, Switzerland) · 2026Article
- GeoPep: A Geometry-Aware Masked Language Model for Protein-Peptide Binding Site Prediction.Journal of chemical information and modeling · 2026Article
- Context-dependent peptide recognition shapes tyrosine kinase substrate specificity beyond consensus motifs.bioRxiv : the preprint server for biology · 2026Article
- Contemporary data-driven innovations in peptide-based therapeutic design.Briefings in bioinformatics · 2026Review
- ProVenTL: a transfer-learning framework for predicting peptide-protein interactions derived from snake venom for cancer therapeutics.Journal of computer-aided molecular design · 2026Article
- Computational screening of AI-derived cyclotides as putative VEGFR2 binders for wound-site angiogenesis.Scientific reports · 2026Article
- A Review of Current Computational Tools for Peptide-Protein Docking.Journal of computational chemistry · 2026Review
- Bioactive peptides targeting dyslipidemia and atherosclerosis: from computational discovery and data bottlenecks to precision medicine translation.Frontiers in molecular biosciences · 2026Review
- Artificial intelligence in peptide cancer vaccine design: from neoantigen discovery to immunogenicity prediction.Frontiers in genetics · 2026Review
- Recent advances in multimodal foundation model-enabled peptide screening and optimization for smart biomaterials and functional tissue engineering.Frontiers in bioengineering and biotechnology · 2026Review
- Predicting Protein-Protein Interactions from Machine-Learned Representations.Advances in experimental medicine and biology · 2026Review
- In silico discovery of umami peptides: From sequence-based intelligent models to structure-function mechanistic insight.Current research in food science · 2026Review
- Accurate site-specific folding via conditional diffusion based on AlphaFold3.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Accurate affinity models for SH2 domains from peptide binding assays and free-energy regression.Protein science : a publication of the Protein Society · 2025Article
- Automated drug design for druggable target identification using integrated stacked autoencoder and hierarchically self-adaptive optimization.Scientific reports · 2025Article
- A Computational Perspective to Intermolecular Interactions and the Role of the Solvent on Regulating Protein Properties.Chemical reviews · 2025Review
- Accurate Site-specific Folding via Conditional Diffusion Based on Alphafold3.bioRxiv : the preprint server for biology · 2025Article
- Chemical Protein Engineering: Backbone Cyclization Rescues Folding of a 183-Residue Truncated Domain of Malaria Parasite Protein PfAMA1.Chemistry (Weinheim an der Bergstrasse, Germany) · 2025Article
- Review
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Authors and funding
3 authors.
Funding
Abstract
Peptides play a pivotal role in a wide range of biological activities through participating in up to 40% protein-protein interactions in cellular processes. They also demonstrate remarkable specificity and efficacy, making them promising candidates for drug development. However, predicting peptide-protein complexes by traditional computational approaches, such as docking and molecular dynamics simulations, still remains a challenge due to high computational cost, flexible nature of peptides, and limited structural information of peptide-protein complexes. In recent years, the surge of available biological data has given rise to the development of an increasing number of machine learning models for predicting peptide-protein interactions. These models offer efficient solutions to address the challenges associated with traditional computational approaches. Furthermore, they offer enhanced accuracy, robustness, and interpretability in their predictive outcomes. This review presents a comprehensive overview of machine learning and deep learning models that have emerged in recent years for the prediction of peptide-protein interactions.
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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.