ArticleACS medicinal chemistry letters2022
Large-Scale Screening of Antifungal Peptides Based on Quantitative Structure-Activity Relationship.
Article in ACS medicinal chemistry letters, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
What it found
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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
19 citing papers in PubMed.
- Integrative computational-experimental discovery and translation of antifungal peptides for multidrug-resistant fungi.Frontiers in microbiology · 2026Review
- A scalable reinforcement learning approach for screening large peptide libraries for bioactive peptide discovery.Nature communications · 2025Article
- Novel antibacterial peptide HfAMP against methicillin-resistant Staphylococcus aureus.BMC microbiology · 2025Article
- Deep Learning Combined with Quantitative Structure‒Activity Relationship Accelerates De Novo Design of Antifungal Peptides.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- AMPed up immunity: 418 whole genomes reveal intraspecific diversity of koala antimicrobial peptides.Immunogenetics · 2025Article
- Article
- Detection of antimicrobial peptides from fecal samples of FMT donors using deep learning.Frontiers in veterinary science · 2025Article
- Role of Peptide Associations in Enhancing the Antimicrobial Activity of Adepantins: Comparative Molecular Dynamics Simulations and Design Assessments.International journal of molecular sciences · 2024Article
- Protein Language Models and Machine Learning Facilitate the Identification of Antimicrobial Peptides.International journal of molecular sciences · 2024Article
- Translocation of Antimicrobial Peptides across Model Membranes: The Role of Peptide Chain Length.Molecular pharmaceutics · 2024Article
- PGAT-ABPp: harnessing protein language models and graph attention networks for antibacterial peptide identification with remarkable accuracy.Bioinformatics (Oxford, England) · 2024Article
- Machine Learning Accelerates De Novo Design of Antimicrobial Peptides.Interdisciplinary sciences, computational life sciences · 2024Article
- The antibacterial activity and mechanism of a novel peptide MR-22 against multidrug-resistantFrontiers in cellular and infection microbiology · 2024Article
- DeepAFP: An effective computational framework for identifying antifungal peptides based on deep learning.Protein science : a publication of the Protein Society · 2023Article
- Prediction of Antifungal Activity of Antimicrobial Peptides by Transfer Learning from Protein Pretrained Models.International journal of molecular sciences · 2023Article
- Specific Focus on Antifungal Peptides against Azole ResistantJournal of fungi (Basel, Switzerland) · 2022Review
- The Antimicrobial Peptide AMP-17 Derived fromAntibiotics (Basel, Switzerland) · 2022Article
- Designed Multifunctional Peptides for Intracellular Targets.Antibiotics (Basel, Switzerland) · 2022Review
- New strategy for clinical etiologic diagnosis of acute ischemic stroke and blood biomarker discovery based on machine learning.RSC advances · 2022Article
Corrections and comments
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Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Antifungal peptides are effective, biocompatible, and biodegradable, and thus, they are promising to be the next generation of drugs for treating infections caused by fungi. The identification processes of highly active peptides, however, are still time-consuming and labor-intensive. Quantitative structure-activity relationships (QSARs) have dramatically facilitated the discovery of many bioactive drug molecules without
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.