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QSAR modeling and machine learning for antifungal peptide discovery in crop protection: Translational challenges and future perspectives
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DOI：10.1016/j.jafr.2026.103162.png)
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Abstract
En 中文
• Machine learning (ML) accelerates antifungal peptide discovery workflows. • QSAR models enable rational design of crop-protective peptides. • Most antifungal peptides (AFPs) predictive models focus on human rather than plant pathogens. • Generative AI expands peptide chemical space beyond natural sequences. • Plant-specific QSAR-ML datasets are needed for agricultural applications. • AFPs offer sustainable alternatives to conventional fungicides. • Multi-objective ML design may improve peptide stability and delivery. • AI-guided AFPs discovery could support next-generation biofungicides.
Keywords:
Antifungal peptides
QSAR
Machine learning
Protein language models
Biopesticides
Crop protection
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