ArticleAdvanced science (Weinheim, Baden-Wurttemberg, Germany)2025
Deep Learning Combined with Quantitative Structure‒Activity Relationship Accelerates De Novo Design of Antifungal Peptides.
Article in Advanced science (Weinheim, Baden-Wurttemberg, Germany), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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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
8 citing papers in PubMed.
- Metallo-beta-lactamase α-helix fragment modified peptide N10 is a multifunctional antimicrobial peptide.Microbiology spectrum · 2026Article
- Article
- Antifungal peptides: From modes of action to synergistic and immunologic potential.Cell stress · 2026Article
- Repositioning Antimicrobial Peptides Against WHO-Priority Fungi.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Review
- Deep Learning Combined with Quantitative Structure‒Activity Relationship Accelerates De Novo Design of Antifungal Peptides.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- 2024 FDA TIDES (Peptides and Oligonucleotides) Harvest.Pharmaceuticals (Basel, Switzerland) · 2025Review
- New antifungal strategies and drug development against WHO critical priority fungal pathogens.Frontiers in cellular and infection microbiology · 2025Review
- Epidemiological shifts and advances in research on early diagnosis of invasive fungal infection in critically ill patients.Frontiers in cellular and infection microbiology · 2025Review
Corrections and comments
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Authors and funding
8 authors.
Funding
Abstract
Novel antifungal drugs that evade resistance are urgently needed for Candida infections. Antifungal peptides (AFPs) are potential candidates due to their specific mechanism of action, which makes them less prone to developing drug resistance. An AFP de novo design method, Deep Learning-Quantitative Structure‒Activity Relationship Empirical Screening (DL-QSARES), is developed by integrating deep learning and quantitative structure‒activity relationship empirical screening. After generating candidate AFPs (c_AFPs) through the recombination of dominant amino acids and dipeptide compositions, natural language processing models are utilized and quantitative structure‒activity relationship (QSAR) approaches based on physicochemical properties to screen for promising c_AFPs. Forty-nine promising c_AFPs are screened, and their minimum inhibitory concentrations (MICs) against C. albicans are determined to be 3.9-125 µg mL
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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.