ArticleScientific reports2020
AnOxPePred: using deep learning for the prediction of antioxidative properties of peptides.
Article in Scientific reports, 2020. 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
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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
73 citing papers in PubMed.
- Suspect screening-based LC-HRMS characterization of short-chain peptide hydrolysates from marine macroalgae.Analytical and bioanalytical chemistry · 2026Article
- LC-MS/MS-based peptide identification, molecular simulations, and biological validation for targeted discovery of antioxidant peptides from large yellow croaker.Food chemistry: X · 2026Article
- Effects of DietaryAnimals : an open access journal from MDPI · 2026Article
- Leakage-Aware Machine Learning and Deep Learning Benchmarking of Food Antioxidant Capacity Prediction on the Antioxidant Food Table.Antioxidants (Basel, Switzerland) · 2026Article
- Identification and In Silico Selection of Novel Antioxidant Peptides from Asian Swamp Eel Bone: Quantum Chemical Calculations, Molecular Docking, and Zebrafish Model Validation.Antioxidants (Basel, Switzerland) · 2026Article
- Comprehensive Characterization and Antioxidant Function Prediction of Endogenous and Exogenous Peptides fromCurrent issues in molecular biology · 2026Article
- RLAnOxPeptide: an integrated framework combining transformer and reinforcement learning for efficient antioxidant peptide prediction and innovative design.Bioinformatics (Oxford, England) · 2026Article
- Unveiling novel antioxidant peptides from silver carp (Food chemistry: X · 2026Article
- Marine Peptides fromNutrients · 2026Article
- PepPharmaHub: a cloud-based platform integrating multimodel language architectures with curated data resources for therapeutic peptide discovery.BMC biology · 2026Article
- Design of Multifunctional Bioactive Peptides for Potential Application in Food Packaging: A Machine-Learning-Integrated Optimization Framework.ACS omega · 2026Article
- Enhancing the Recovery of Antioxidant Compounds from Microalgae-Cyanobacteria Consortia Through Alcalase Hydrolysis: A Focus on Bioactive Peptides.Marine drugs · 2026Article
- A tri-modal contrastive learning framework for protein representation learning.Cell reports methods · 2026Article
- NeuroPred-GMC: a dual-branch deep learning architecture for neuropeptide prediction based on gated dilated convolutional network and multi-scale convolutional network.Journal of computer-aided molecular design · 2026Article
- Article
- Discovery of Iron-Chelating Peptides fromFoods (Basel, Switzerland) · 2026Article
- Artificial intelligence-driven discovery of bioactive peptides: Computational approaches and future perspectives.aBIOTECH · 2026Review
- Multi-AOP: a lightweight multi-view deep learning framework for antioxidant peptide discovery.Bioresources and bioprocessing · 2026Article
- Antioxidant potential and genomic adaptation ofFrontiers in microbiology · 2026Article
- Rational discovery of peptide inhibitors targeting Keap1: a focus on neurodegenerative diseases.Frontiers in bioinformatics · 2026Article
13 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
11 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Dietary antioxidants are an important preservative in food and have been suggested to help in disease prevention. With consumer demands for less synthetic and safer additives in food products, the food industry is searching for antioxidants that can be marketed as natural. Peptides derived from natural proteins show promise, as they are generally regarded as safe and potentially contain other beneficial bioactivities. Antioxidative peptides are usually obtained by testing various peptides derived from hydrolysis of proteins by a selection of proteases. This slow and cumbersome trial-and-error approach to identify antioxidative peptides has increased interest in developing computational approaches for prediction of antioxidant activity and thereby reduce laboratory work. A few antioxidant predictors exist, however, no tool predicting the antioxidative properties of peptides is, to the best of our knowledge, currently available as a web-server. We here present the AnOxPePred tool and web-server ( http://services.bioinformatics.dtu.dk/service.php?AnOxPePred-1.0 ) that uses deep learning to predict the antioxidant properties of peptides. Our model was trained on a curated dataset consisting of experimentally-tested antioxidant and non-antioxidant peptides. For a variety of metrics our method displays a prediction performance better than a k-NN sequence identity-based approach. Furthermore, the developed tool will be a good benchmark for future predictors of antioxidant peptides.
Indexed as
Identifiers
What OpenQuestion holds
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.