Machine Learning- and AI-Driven QSAR Models for the Discovery of Novel Potential Fungicides: FAPI (Fungicide Targeting Acid Phosphatase Inhibition).

ABSTRACT: Fungal pathogens like Podosphaera xanthii (powdery mildew) and Botrytis cinerea (gray mold) cause significant agricultural losses, with fungicide resistance escalating due to the overreliance on conventional treatments. Consequently, the development of sustainable alternatives with novel modes of action is imperative for future crop protection. Acid phosphatases (APs), which play a key role in fungal phosphate metabolism and virulence, have emerged as promising molecular targets. To accelerate the identification of fungicides targeting acid phosphatase inhibition (FAPI), machine learning (ML), and artificial intelligence (AI)-driven quantitative structure-activity relationship (QSAR) models incorporating topological molecular descriptors have been employed to predict fungicid

SUBMITTER: Galvez-Llompart M 

PROVIDER: S-EPMC12814334 | biostudies-literature | 2026 Jan

REPOSITORIES: biostudies-literature