ArticleJournal of advanced research2026
Generation of antifungals to combat drug resistance using language models and diffusion models.
Article in Journal of advanced research, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
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Who cites it
2 citing papers in PubMed.
- Identification of GPI-Anchored Wall Transfer Protein 1 Modulators for Fungal Infections Through Generative AI and Physics-Based Approaches.International journal of molecular sciences · 2026Article
- Unlocking the potential of computational phenotypic drug discovery: methods, challenges, and future directions.NPJ systems biology and applications · 2026Review
Corrections and comments
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Authors and funding
11 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
introductionThe growing scarcity of effective antifungal agents, coupled with rising drug‑resistance, creates an urgent demand for novel therapeutics. Recent advances in artificial intelligence (AI) have opened new avenues for accelerating small‑molecule discovery, offering a promising strategy to meet this challenge.
objectivesThis study aimed to develop an AI-driven pipeline capable of rapidly generating biologically active inhibitors targeting antifungal proteins and to evaluate the therapeutic potential of the resulting candidates.
methodsWe developed a diffusion-based generative platform, MolDiffusion, to design small molecules against both single- and dual-target profiles. The pipeline was applied to five fungal protein targets and one human protein Keap1. Top-ranked candidate molecules were purchased and evaluated through biochemical assays, cell-based inhibition tests, and in vivo studies using a murine candidiasis model.
resultsApproximately 50% of the candidates generated by the MolDiffusion pipeline exhibited measurable activity in vitro. The platform successfully yielded both single-target and dual-target hits. Notably, two compounds demonstrated significant in vivo efficacy in the mouse model of candidiasis.
conclusionMolDiffusion effectively translates AI-generated molecular designs into experimentally validated antifungal leads, including dual-target compounds with potential to overcome drug resistance. These findings highlight the platform's promise as a robust tool for next-generation antifungal drug discovery.
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Registered trials
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