An Artificial Intelligence-Driven Antifungal Drug Target Discovery Platform (360G-Wellcome-348060_Z_26_Z)

Fungal antimicrobial resistance is a growing global health threat, particularly from critical priority pathogens such as Aspergillus fumigatus and Candida auris. There is a clear unmet need for novel antifungal agents with new mechanisms, low toxicity, and high resistance barriers. We propose an integrated, artificial intelligence (AI)-driven antifungal drug discovery platform to systematically identify and validate novel drug targets. Our approach harnesses cutting-edge technologies, CRISPR-based oligo recombineering, cysteine-reactivity profiling, and AI-guided prediction of gene essentiality, toxicity, and resistance emergence, to accelerate the discovery of durable antifungal agents. This platform will focus on C. auris, building genome-wide libraries and targetable chemogenomic maps. Structure- guided lead discovery will be supported by state-of-the-art AI pipelines and compound libraries. Predictive models for toxicity and resistance will ensure prioritization of safe and durable drug targets. Pangenome approaches will explore the evolutionary constraints of target sites; bolstering binding efficacy and target specificity, whilst enabling detection of emerging resistance. Expected outcomes include the identification of novel, non-human homologous targets; public release of chemogenomic and resistance prediction datasets; and an advanced AI-enabled pipeline adaptable to emerging and future antifungal threats. This project will transform antifungal discovery, future- proof therapies against resistance, and deliver early target enablement.