ReviewFrontiers in cellular and infection microbiology2025
AI in fungal drug development: opportunities, challenges, and future outlook.
Review in Frontiers in cellular and infection microbiology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 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
16 citing papers in PubMed.
- Pharmacological advances inJournal of enzyme inhibition and medicinal chemistry · 2026Review
- Machine learning-guided QSAR screening of fluconazole analogs and FDA-approved drugs against Candida albicans, with docking, molecular dynamics, and ADMET analysis.Molecular diversity · 2026Article
- Fung-AI: An AI/ML-driven pipeline for antifungal peptide discovery.PLoS computational biology · 2026Article
- AI-driven fungicide design: From target identification to field application.Plant communications · 2026Review
- How post-translational modifications in pathogenic fungi inform pathogenesis and immune responses.PLoS pathogens · 2026Article
- Artificial Intelligence-Driven Discovery and Optimization of Antimicrobial Peptides Targeting ESKAPE Pathogens and Multidrug-Resistant Fungi.Microorganisms · 2026Review
- Molecular Identification and RNA-Based Management of Fungal Plant Pathogens: From PCR to CRISPR/Cas9.International journal of molecular sciences · 2026Review
- Decoding the rhizosphere microbiome againstFrontiers in microbiomes · 2026Review
- Formulation-Driven Innovation in Antifungal Therapy: From Nanotechnology to AI-Assisted Design.International journal of nanomedicine · 2026Review
- A quiet but growing threat: Global perspectives on antifungal resistance and future therapeutic approaches.Indian journal of pharmacology · 2026Review
- From the Ground to the Clinic: The Evolution and Adaptation of Fungi.Journal of fungi (Basel, Switzerland) · 2025Review
- Unravelling azole resistance in fungal pathogens: molecular mechanisms, diagnostic challenges, and therapeutic strategies.World journal of microbiology & biotechnology · 2025Review
- The kinase Bud32 regulates iron homeostasis in fungal pathogenFrontiers in immunology · 2025Article
- Regulatory functions of AcuK and AcuM transcription factors in fungal metabolic adaptation, stress response, and virulence.Frontiers in cellular and infection microbiology · 2025Review
- Research advances and public health strategies in China on WHO priority fungal pathogens.Mycology · 2025Review
- New antifungal strategies and drug development against WHO critical priority fungal pathogens.Frontiers in cellular and infection microbiology · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The application of artificial intelligence (AI) in fungal drug development offers innovative strategies to address the escalating threat of fungal infections and the challenge of antifungal resistance. This review evaluates the current landscape of fungal infections, highlights the limitations of existing antifungal therapies, and examines the transformative potential of AI in drug discovery and development. We specifically focus on how AI can enhance the identification of new antifungal agents and improve therapeutic strategies. Despite numerous opportunities for advancement, significant challenges remain, particularly regarding data quality, regulatory frameworks, and the complexities associated with the drug development process. This review aims to provide insights into recent advancements in AI technologies, their implications for the future of fungal drug development, and the necessary research directions to effectively leverage AI for improved patient outcomes.
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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.