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

TL;DR

This paper uses machine learning and AI to discover new fungicides targeting acid phosphatase in fungi, aiming to combat resistance and improve crop protection.

Contribution

The novel use of ML/AI-driven QSAR models to identify fungicides targeting acid phosphatase inhibition.

Findings

Machine learning and AI models successfully predicted fungicidal activity against acid phosphatase.

Experimental validation confirmed the potential of predicted fungicides as resistance-breaking agents.

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 fungicidal
activity. The experimental validation of the predicted candidates
highlights the promising potential of these novel fungicides and underscores
the value of ML- and AI-based QSAR methodologies in the development
of next-generation, resistance-breaking fungicidal agents.

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