ArticleMolecules (Basel, Switzerland)2019
Quantitative Structure-Activity Relationship Study of Bitter Di-, Tri- and Tetrapeptides Using Integrated Descriptors.
Article in Molecules (Basel, Switzerland), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 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
6 citing papers in PubMed, 23 citations in OpenAlex.
- Research Progress on Intelligent Prediction, Debittering Technologies, and Multi-Dimensional Evaluation for Bitter Peptides.Foods (Basel, Switzerland) · 2026Review
- Research on Bitter Peptides in the Field of Bioinformatics: A Comprehensive Review.International journal of molecular sciences · 2024Review
- Bitter-RF: A random forest machine model for recognizing bitter peptides.Frontiers in medicine · 2023Article
- Characteristics of the enzyme-induced release of bitter peptides from wheat gluten hydrolysates.Frontiers in nutrition · 2022Article
- iBitter-Fuse: A Novel Sequence-Based Bitter Peptide Predictor by Fusing Multi-View Features.International journal of molecular sciences · 2021Article
- In Silico Rational Design and Virtual Screening of Bioactive Peptides Based on QSAR Modeling.ACS omega · 2020Article
Corrections and comments
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Authors and funding
2 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
New quantitative structure-activity relationship (QSAR) models for bitter peptides were built with integrated amino acid descriptors. Datasets contained 48 dipeptides, 52 tripeptides and 23 tetrapeptides with their reported bitter taste thresholds. Independent variables consisted of 14 amino acid descriptor sets. A bootstrapping soft shrinkage approach was utilized for variable selection. The importance of a variable was evaluated by both variable selecting frequency and standardized regression coefficient. Results indicated model qualities for di-, tri- and tetrapeptides with R
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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.