ReviewFrontiers in chemistry2026
Green toxicology only becomes beautiful through AI.
Review in Frontiers in chemistry, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Green Toxicology extends the principles of Green Chemistry by embedding toxicological foresight into chemical design, with the aim of preventing hazards before substances reach markets or the environment. Its conceptual pillars-prevention, precaution, life-cycle thinking, and avoidance of regrettable substitutions-align closely with sustainability agendas such as the European Green Deal and the United Nations Sustainable Development Goals. Despite its promise, Green Toxicology has remained largely aspirational, limited by fragmented data, slow regulatory uptake, and reliance on new approach methodologies (NAMs) that still face validation and acceptance hurdles. Artificial intelligence (AI) offers a transformative solution by integrating heterogeneous datasets, enhancing predictive accuracy, and enabling probabilistic risk assessment. Deep learning, natural language processing, and explainable AI can mine legacy studies, link adverse outcome pathways, and design safer chemistries proactively. Coupled with microphysiological systems and omics, AI makes Green Toxicology predictive, human-relevant, and scalable. Together, they form a practical framework for guiding chemical innovation toward sustainability, reconciling industrial productivity with ecological integrity and public health protection.
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.