ReviewPlant communications2026
AI-driven fungicide design: From target identification to field application.
Review in Plant communications, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
- Decoding the rhizosphere microbiome againstFrontiers in microbiomes · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
Plant pathogenic fungi pose a severe threat to global agriculture, causing substantial yield losses in staple crops and jeopardizing food safety through mycotoxin contamination. Conventional fungicide development is hindered by high costs, lengthy timelines, and the rapid evolution of fungal resistance, which outpaces conventional discovery workflows. Although artificial intelligence (AI) offers transformative potential to address these bottlenecks, its application in plant pathology remains fragmented and lacks integration of agriculture-specific constraints such as field stability, ecological safety, and resistance management. This review introduces the AI-driven fungicide design (AIFD) platform, a comprehensive framework comprising four interdependent components: a plant pathogen-specific data ecosystem, a modular microservice technical architecture, a linear multiphase development workflow, and a specialized resistance prediction workflow. We synthesize key technological advances across the fungicide development pipeline, from target identification and virtual screening to molecular optimization and field validation, with an emphasis on AI methodologies adapted to agrochemical requirements rather than pharmaceutical standards. Despite substantial advances, critical challenges persist, including scarce high-quality training data for understudied pathogens, limited model adaptability across diverse agroecosystems, poor interpretability that hinders stakeholder trust, and accessibility barriers for resource-constrained researchers. Future directions emphasize the integration of real-time field data, explainable AI to facilitate regulatory acceptance, and inclusive design strategies aimed at bridging the laboratory-to-field gap. By aligning computational innovation with agricultural priorities, AIFD platforms can accelerate the discovery of resistance-breaking, environmentally benign fungicides, thus offering a viable pathway toward sustainable crop protection and enhanced global food security.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.